Add label_data property to pipeline

spacy/pipeline/morphologizer.pyx

@@ -1,5 +1,5 @@
 # cython: infer_types=True, profile=True, binding=True
-from typing import Optional
+from typing import Optional, Union, Dict
 import srsly
 from thinc.api import SequenceCategoricalCrossentropy, Model, Config
 from itertools import islice
@@ -101,6 +101,11 @@ class Morphologizer(Tagger):
         """RETURNS (Tuple[str]): The labels currently added to the component."""
         return tuple(self.cfg["labels_morph"].keys())
 
+    @property
+    def label_data(self) -> Dict[str, Dict[str, Union[str, float, int, None]]]:
+        """RETURNS (Dict): A dictionary with all labels data."""
+        return {"morph": self.cfg["labels_morph"], "pos": self.cfg["labels_pos"]}
+
     def add_label(self, label):
         """Add a new label to the pipe.
 

spacy/pipeline/pipe.pyx

@@ -1,4 +1,5 @@
 # cython: infer_types=True, profile=True
+from typing import Optional, Tuple
 import srsly
 from thinc.api import set_dropout_rate, Model
 
@@ -32,6 +33,20 @@ cdef class Pipe:
         self.name = name
         self.cfg = dict(cfg)
 
+    @property
+    def labels(self) -> Optional[Tuple[str]]:
+        if "labels" in self.cfg:
+            return tuple(self.cfg["labels"])
+        else:
+            return None
+    
+    @property
+    def label_data(self):
+        """Optional JSON-serializable data that would be sufficient to recreate
+        the label set if provided to the `pipe.initialize()` method.
+        """
+        return None
+
     def __call__(self, Doc doc):
         """Apply the pipe to one document. The document is modified in place,
         and returned. This usually happens under the hood when the nlp object

spacy/pipeline/senter.pyx

@@ -71,6 +71,10 @@ class SentenceRecognizer(Tagger):
         # are 0
         return tuple(["I", "S"])
 
+    @property
+    def label_data(self):
+        return self.labels
+
     def set_annotations(self, docs, batch_tag_ids):
         """Modify a batch of documents, using pre-computed scores.
 

spacy/pipeline/tagger.pyx

@@ -90,6 +90,16 @@ class Tagger(Pipe):
         """
         return tuple(self.cfg["labels"])
 
+    @property
+    def label_data(self):
+        """Data about the labels currently added to the component.
+
+        RETURNS (Dict): The labels data.
+
+        DOCS: https://nightly.spacy.io/api/tagger#labels
+        """
+        return tuple(self.cfg["labels"])
+
     def __call__(self, doc):
         """Apply the pipe to a Doc.
 

spacy/pipeline/textcat.py

@@ -154,8 +154,23 @@ class TextCategorizer(Pipe):
 
     @labels.setter
     def labels(self, value: List[str]) -> None:
+        # TODO: This really shouldn't be here. I had a look and I added it when
+        # I added the labels property, but it's pretty nasty to have this, and
+        # will lead to problems.
         self.cfg["labels"] = tuple(value)
 
+    @property
+    def label_data(self) -> Dict:
+        """RETURNS (Dict): Information about the component's labels.
+
+        DOCS: https://nightly.spacy.io/api/textcategorizer#labels
+        """
+        return {
+            "labels": self.labels,
+            "positive": self.cfg["positive_label"],
+            "threshold": self.cfg["threshold"]
+        }
+
     def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
         """Apply the pipe to a stream of documents. This usually happens under
         the hood when the nlp object is called on a text and all components are

spacy/pipeline/transition_parser.pyx

@@ -95,6 +95,10 @@ cdef class Parser(Pipe):
         class_names = [self.moves.get_class_name(i) for i in range(self.moves.n_moves)]
         return class_names
 
+    @property
+    def label_data(self):
+        return self.moves.labels
+
     @property
     def tok2vec(self):
         """Return the embedding and convolutional layer of the model."""