Fix import

spacy/pipeline/senter.pyx

@@ -3,6 +3,7 @@ from itertools import islice
 
 import srsly
 from thinc.api import Model, SequenceCategoricalCrossentropy, Config
+from thinc.api import set_dropout_rate
 
 from ..tokens.doc cimport Doc
 
@@ -95,6 +96,49 @@ class SentenceRecognizer(Tagger):
                     else:
                         doc.c[j].sent_start = -1
 
+    def update(self, examples, *, drop=0., sgd=None, losses=None, set_annotations=False):
+        """Learn from a batch of documents and gold-standard information,
+        updating the pipe's model. Delegates to predict and get_loss.
+
+        examples (Iterable[Example]): A batch of Example objects.
+        drop (float): The dropout rate.
+        set_annotations (bool): Whether or not to update the Example objects
+            with the predictions.
+        sgd (thinc.api.Optimizer): The optimizer.
+        losses (Dict[str, float]): Optional record of the loss during training.
+            Updated using the component name as the key.
+        RETURNS (Dict[str, float]): The updated losses dictionary.
+
+        DOCS: https://nightly.spacy.io/api/tagger#update
+        """
+        if losses is None:
+            losses = {}
+        losses.setdefault(self.name, 0.0)
+        validate_examples(examples, "Tagger.update")
+        if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
+            # Handle cases where there are no tokens in any docs.
+            return
+        if not any(eg.reference.is_sentenced for eg in examples):
+            # Handle cases where there are no tagged tokens in any docs.
+            return
+        set_dropout_rate(self.model, drop)
+        tag_scores, bp_tag_scores = self.model.begin_update([eg.predicted for eg in examples])
+        for sc in tag_scores:
+            if self.model.ops.xp.isnan(sc.sum()):
+                raise ValueError(Errors.E940)
+        loss, d_tag_scores = self.get_loss(examples, tag_scores)
+        bp_tag_scores(d_tag_scores)
+        if sgd not in (None, False):
+            self.model.finish_update(sgd)
+
+        losses[self.name] += loss
+        if set_annotations:
+            docs = [eg.predicted for eg in examples]
+            self.set_annotations(docs, self._scores2guesses(tag_scores))
+        return losses
+
+
+
     def get_loss(self, examples, scores):
         """Find the loss and gradient of loss for the batch of documents and
         their predicted scores.

spacy/pipeline/tagger.pyx

@@ -192,6 +192,9 @@ class Tagger(Pipe):
         if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
             # Handle cases where there are no tokens in any docs.
             return
+        if not any(eg.reference.is_tagged for eg in examples):
+            # Handle cases where there are no tagged tokens in any docs.
+            return
         set_dropout_rate(self.model, drop)
         tag_scores, bp_tag_scores = self.model.begin_update([eg.predicted for eg in examples])
         for sc in tag_scores:
@@ -251,7 +254,11 @@ class Tagger(Pipe):
         DOCS: https://nightly.spacy.io/api/tagger#get_loss
         """
         validate_examples(examples, "Tagger.get_loss")
-        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
+        loss_func = SequenceCategoricalCrossentropy(
+            names=self.labels,
+            normalize=False,
+            missing_value=""
+        )
         truths = [eg.get_aligned("TAG", as_string=True) for eg in examples]
         d_scores, loss = loss_func(scores, truths)
         if self.model.ops.xp.isnan(loss):