more friendly textcat errors (#3946)

* more friendly textcat errors with require_model and require_labels

* update thinc version with recent bugfix

requirements.txt

@@ -1,7 +1,7 @@
 # Our libraries
 cymem>=2.0.2,<2.1.0
 preshed>=2.0.1,<2.1.0
-thinc>=7.0.2,<7.1.0
+thinc>=7.0.5,<7.1.0
 blis>=0.2.2,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.2.0,<1.1.0

spacy/errors.py

@@ -403,6 +403,7 @@ class Errors(object):
     E140 = ("The list of entities, prior probabilities and entity vectors should be of equal length.")
     E141 = ("Entity vectors should be of length {required} instead of the provided {found}.")
     E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
+    E143 = ("Labels for component '{name}' not initialized. Did you forget to call add_label()?")
 
 
 @add_codes

spacy/pipeline/pipes.pyx

@@ -902,6 +902,11 @@ class TextCategorizer(Pipe):
     def labels(self):
         return tuple(self.cfg.setdefault("labels", []))
 
+    def require_labels(self):
+        """Raise an error if the component's model has no labels defined."""
+        if not self.labels:
+            raise ValueError(Errors.E143.format(name=self.name))
+
     @labels.setter
     def labels(self, value):
         self.cfg["labels"] = tuple(value)
@@ -931,6 +936,7 @@ class TextCategorizer(Pipe):
                 doc.cats[label] = float(scores[i, j])
 
     def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
+        self.require_model()
         scores, bp_scores = self.model.begin_update(docs, drop=drop)
         loss, d_scores = self.get_loss(docs, golds, scores)
         bp_scores(d_scores, sgd=sgd)
@@ -985,6 +991,7 @@ class TextCategorizer(Pipe):
     def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
         if self.model is True:
             self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors")
+            self.require_labels()
             self.model = self.Model(len(self.labels), **self.cfg)
             link_vectors_to_models(self.vocab)
         if sgd is None: