Prevent Tagger model init with 0 labels (#5984)

* Prevent Tagger model init with 0 labels

Raise an error before trying to initialize a tagger model with 0 labels.

* Add dummy tagger label for test

* Remove tagless tagger model initializiation

* Fix error number after merge

* Add dummy tagger label to test

* Fix formatting

Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>

spacy/errors.py

@@ -651,6 +651,7 @@ class Errors:
     E1005 = ("Unable to set attribute '{attr}' in tokenizer exception for "
              "'{chunk}'. Tokenizer exceptions are only allowed to specify "
              "`ORTH` and `NORM`.")
+    E1006 = ("Unable to initialize {name} model with 0 labels.")
 
 
 @add_codes

spacy/pipeline/tagger.pyx

@@ -285,11 +285,11 @@ class Tagger(Pipe):
             doc_sample.append(Doc(self.vocab, words=["hello"]))
         for tag in sorted(tags):
             self.add_label(tag)
+        if len(self.labels) == 0:
+            err = Errors.E1006.format(name="Tagger")
+            raise ValueError(err)
         self.set_output(len(self.labels))
-        if self.labels:
-            self.model.initialize(X=doc_sample)
-        else:
-            self.model.initialize()
+        self.model.initialize(X=doc_sample)
         if sgd is None:
             sgd = self.create_optimizer()
         return sgd

spacy/tests/pipeline/test_tagger.py

@@ -69,3 +69,10 @@ def test_overfitting_IO():
         assert doc2[1].tag_ is "V"
         assert doc2[2].tag_ is "J"
         assert doc2[3].tag_ is "N"
+
+
+def test_tagger_requires_labels():
+    nlp = English()
+    tagger = nlp.add_pipe("tagger")
+    with pytest.raises(ValueError):
+        optimizer = nlp.begin_training()

spacy/tests/regression/test_issue4001-4500.py

@@ -326,7 +326,8 @@ def test_issue4348():
     nlp = English()
     example = Example.from_dict(nlp.make_doc(""), {"tags": []})
     TRAIN_DATA = [example, example]
-    nlp.add_pipe("tagger")
+    tagger = nlp.add_pipe("tagger")
+    tagger.add_label("A")
     optimizer = nlp.begin_training()
     for i in range(5):
         losses = {}

spacy/tests/regression/test_issue5230.py

@@ -63,6 +63,7 @@ def tagger():
     # need to add model for two reasons:
     # 1. no model leads to error in serialization,
     # 2. the affected line is the one for model serialization
+    tagger.add_label("A")
     tagger.begin_training(lambda: [], pipeline=nlp.pipeline)
     return tagger
 

spacy/tests/serialize/test_serialize_config.py

@@ -144,6 +144,7 @@ def test_serialize_nlp():
     """ Create a custom nlp pipeline from config and ensure it serializes it correctly """
     nlp_config = Config().from_str(nlp_config_string)
     nlp, _ = load_model_from_config(nlp_config, auto_fill=True)
+    nlp.get_pipe("tagger").add_label("A")
     nlp.begin_training()
     assert "tok2vec" in nlp.pipe_names
     assert "tagger" in nlp.pipe_names