Update morphologizer (#5108)

* Add pos and morph scoring to Scorer

Add pos, morph, and morph_per_type to `Scorer`. Report pos and morph
accuracy in `spacy evaluate`.

* Update morphologizer for v3

* switch to tagger-based morphologizer
* use `spacy.HashCharEmbedCNN` for morphologizer defaults
* add `Doc.is_morphed` flag

* Add morphologizer to train CLI

* Add basic morphologizer pipeline tests

* Add simple morphologizer training example

* Remove subword_features from CharEmbed models

Remove `subword_features` argument from `spacy.HashCharEmbedCNN.v1` and
`spacy.HashCharEmbedBiLSTM.v1` since in these cases `subword_features`
is always `False`.

* Rename setting in morphologizer example

Use `with_pos_tags` instead of `without_pos_tags`.

* Fix kwargs for spacy.HashCharEmbedBiLSTM.v1

* Remove defaults for spacy.HashCharEmbedBiLSTM.v1

Remove default `nM/nC` for `spacy.HashCharEmbedBiLSTM.v1`.

* Set random seed for textcat overfitting test

examples/training/train_morphologizer.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python
+# coding: utf8
+"""
+A simple example for training a morphologizer. For more details, see
+the documentation:
+* Training: https://spacy.io/usage/training
+
+Compatible with: spaCy v3.0.0+
+Last tested with: v3.0.0
+"""
+from __future__ import unicode_literals, print_function
+
+import plac
+import random
+from pathlib import Path
+import spacy
+from spacy.util import minibatch, compounding
+from spacy.morphology import Morphology
+
+
+# Usually you'll read this in, of course. Data formats vary. Ensure your
+# strings are unicode and that the number of tags assigned matches spaCy's
+# tokenization. If not, you can always add a 'words' key to the annotations
+# that specifies the gold-standard tokenization, e.g.:
+# ("Eatblueham", {'words': ['Eat', 'blue', 'ham'], 'tags': ['V', 'J', 'N']})
+TRAIN_DATA = [
+    (
+        "I like green eggs",
+        {
+            "morphs": [
+                "PronType=Prs|Person=1",
+                "VerbForm=Fin",
+                "Degree=Pos",
+                "Number=Plur",
+            ],
+            "pos": ["PRON", "VERB", "ADJ", "NOUN"],
+        },
+    ),
+    (
+        "Eat blue ham",
+        {
+            "morphs": ["VerbForm=Inf", "Degree=Pos", "Number=Sing"],
+            "pos": ["VERB", "ADJ", "NOUN"],
+        },
+    ),
+    (
+        "She was blue",
+        {
+            "morphs": ["PronType=Prs|Person=3", "VerbForm=Fin", "Degree=Pos"],
+            "pos": ["PRON", "VERB", "ADJ"],
+        },
+    ),
+    (
+        "He was blue today",
+        {
+            "morphs": ["PronType=Prs|Person=3", "VerbForm=Fin", "Degree=Pos", ""],
+            "pos": ["PRON", "VERB", "ADJ", "ADV"],
+        },
+    ),
+]
+
+# The POS tags are optional, set `with_pos_tags = False` to omit them for
+# this example:
+with_pos_tags = True
+
+if not with_pos_tags:
+    for i in range(len(TRAIN_DATA)):
+        del TRAIN_DATA[i][1]["pos"]
+
+
+@plac.annotations(
+    lang=("ISO Code of language to use", "option", "l", str),
+    output_dir=("Optional output directory", "option", "o", Path),
+    n_iter=("Number of training iterations", "option", "n", int),
+)
+def main(lang="en", output_dir=None, n_iter=25):
+    """Create a new model, set up the pipeline and train the tagger. In order to
+    train the tagger with a custom tag map, we're creating a new Language
+    instance with a custom vocab.
+    """
+    nlp = spacy.blank(lang)
+    # add the tagger to the pipeline
+    # nlp.create_pipe works for built-ins that are registered with spaCy
+    morphologizer = nlp.create_pipe("morphologizer")
+    nlp.add_pipe(morphologizer)
+
+    # add labels
+    for _, annotations in TRAIN_DATA:
+        morph_labels = annotations.get("morphs")
+        pos_labels = annotations.get("pos", [""] * len(annotations.get("morphs")))
+        assert len(morph_labels) == len(pos_labels)
+        for morph, pos in zip(morph_labels, pos_labels):
+            morph_dict = Morphology.feats_to_dict(morph)
+            if pos:
+                morph_dict["POS"] = pos
+            morph = Morphology.dict_to_feats(morph_dict)
+            morphologizer.add_label(morph)
+
+    optimizer = nlp.begin_training()
+    for i in range(n_iter):
+        random.shuffle(TRAIN_DATA)
+        losses = {}
+        # batch up the examples using spaCy's minibatch
+        batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
+        for batch in batches:
+            nlp.update(batch, sgd=optimizer, losses=losses)
+        print("Losses", losses)
+
+    # test the trained model
+    test_text = "I like blue eggs"
+    doc = nlp(test_text)
+    print("Morphs", [(t.text, t.morph) for t in doc])
+
+    # save model to output directory
+    if output_dir is not None:
+        output_dir = Path(output_dir)
+        if not output_dir.exists():
+            output_dir.mkdir()
+        nlp.to_disk(output_dir)
+        print("Saved model to", output_dir)
+
+        # test the save model
+        print("Loading from", output_dir)
+        nlp2 = spacy.load(output_dir)
+        doc = nlp2(test_text)
+        print("Morphs", [(t.text, t.morph) for t in doc])
+
+
+if __name__ == "__main__":
+    plac.call(main)
+
+# Expected output:
+# Morphs [('I', POS=PRON|Person=1|PronType=Prs), ('like', POS=VERB|VerbForm=Fin), ('blue', Degree=Pos|POS=ADJ), ('eggs', Number=Plur|POS=NOUN)]

spacy/cli/evaluate.py

@@ -43,7 +43,9 @@ def evaluate(
         "Words": nwords,
         "Words/s": f"{nwords / (end - begin):.0f}",
         "TOK": f"{scorer.token_acc:.2f}",
-        "POS": f"{scorer.tags_acc:.2f}",
+        "TAG": f"{scorer.tags_acc:.2f}",
+        "POS": f"{scorer.pos_acc:.2f}",
+        "MORPH": f"{scorer.morphs_acc:.2f}",
         "UAS": f"{scorer.uas:.2f}",
         "LAS": f"{scorer.las:.2f}",
         "NER P": f"{scorer.ents_p:.2f}",

spacy/cli/train.py

@@ -221,6 +221,8 @@ def train(
                 config_loc = default_dir / "parser_defaults.cfg"
             elif pipe == "tagger":
                 config_loc = default_dir / "tagger_defaults.cfg"
+            elif pipe == "morphologizer":
+                config_loc = default_dir / "morphologizer_defaults.cfg"
             elif pipe == "ner":
                 config_loc = default_dir / "ner_defaults.cfg"
             elif pipe == "textcat":
@@ -590,6 +592,8 @@ def _score_for_model(meta):
     acc = meta["accuracy"]
     if "tagger" in pipes:
         mean_acc.append(acc["tags_acc"])
+    if "morphologizer" in pipes:
+        mean_acc.append((acc["morphs_acc"] + acc["pos_acc"]) / 2)
     if "parser" in pipes:
         mean_acc.append((acc["uas"] + acc["las"]) / 2)
     if "ner" in pipes:
@@ -672,13 +676,15 @@ def _find_best(experiment_dir, component):
 
 def _get_metrics(component):
     if component == "parser":
-        return ("las", "uas", "las_per_type", "token_acc", "sent_f")
+        return ("las", "uas", "las_per_type", "sent_f", "token_acc")
     elif component == "tagger":
         return ("tags_acc", "token_acc")
+    elif component == "morphologizer":
+        return ("morphs_acc", "pos_acc", "token_acc")
     elif component == "ner":
         return ("ents_f", "ents_p", "ents_r", "ents_per_type", "token_acc")
     elif component == "senter":
-        return ("sent_f", "sent_p", "sent_r")
+        return ("sent_f", "sent_p", "sent_r", "token_acc")
     elif component == "textcat":
         return ("textcat_score", "token_acc")
     return ("token_acc",)
@@ -691,6 +697,9 @@ def _configure_training_output(pipeline, use_gpu, has_beam_widths):
         if pipe == "tagger":
             row_head.extend(["Tag Loss ", " Tag %  "])
             output_stats.extend(["tag_loss", "tags_acc"])
+        elif pipe == "morphologizer" or pipe == "morphologizertagger":
+            row_head.extend(["Morph Loss ", " Morph %  ", " POS % "])
+            output_stats.extend(["morph_loss", "morphs_acc", "pos_acc"])
         elif pipe == "parser":
             row_head.extend(
                 ["Dep Loss ", " UAS  ", " LAS  ", "Sent P", "Sent R", "Sent F"]
@@ -731,6 +740,7 @@ def _get_progress(
     scores["dep_loss"] = losses.get("parser", 0.0)
     scores["ner_loss"] = losses.get("ner", 0.0)
     scores["tag_loss"] = losses.get("tagger", 0.0)
+    scores["morph_loss"] = losses.get("morphologizer", 0.0)
     scores["textcat_loss"] = losses.get("textcat", 0.0)
     scores["senter_loss"] = losses.get("senter", 0.0)
     scores["cpu_wps"] = cpu_wps

spacy/ml/models/defaults/morphologizer_defaults.cfg

@@ -9,6 +9,5 @@ depth = 4
 embed_size = 7000
 window_size = 1
 maxout_pieces = 3
-subword_features = true
 nM = 64
 nC = 8

spacy/ml/models/tok2vec.py

@@ -74,7 +74,6 @@ def hash_charembed_cnn(
     embed_size,
     maxout_pieces,
     window_size,
-    subword_features,
     nM,
     nC,
 ):
@@ -87,7 +86,7 @@ def hash_charembed_cnn(
         bilstm_depth=0,
         maxout_pieces=maxout_pieces,
         window_size=window_size,
-        subword_features=subword_features,
+        subword_features=False,
         char_embed=True,
         nM=nM,
         nC=nC,
@@ -116,7 +115,7 @@ def hash_embed_bilstm_v1(
 
 @registry.architectures.register("spacy.HashCharEmbedBiLSTM.v1")
 def hash_char_embed_bilstm_v1(
-    pretrained_vectors, width, depth, embed_size, subword_features, nM, nC, maxout_pieces
+    pretrained_vectors, width, depth, embed_size, maxout_pieces, nM, nC
 ):
     # Allows using character embeddings by setting nC, nM and char_embed=True
     return build_Tok2Vec_model(
@@ -127,7 +126,7 @@ def hash_char_embed_bilstm_v1(
         conv_depth=0,
         maxout_pieces=maxout_pieces,
         window_size=1,
-        subword_features=subword_features,
+        subword_features=False,
         char_embed=True,
         nM=nM,
         nC=nC,

spacy/pipeline/morphologizer.pyx

@@ -1,166 +1,169 @@
+# cython: infer_types=True, profile=True
 cimport numpy as np
 
 import numpy
-from collections import defaultdict
+import srsly
-from thinc.api import chain, list2array, to_categorical, get_array_module
+from thinc.api import to_categorical
-from thinc.util import copy_array
 
 from ..tokens.doc cimport Doc
 from ..vocab cimport Vocab
 from ..morphology cimport Morphology
+from ..parts_of_speech import IDS as POS_IDS
+from ..symbols import POS
 
 from .. import util
 from ..language import component
 from ..util import link_vectors_to_models, create_default_optimizer
 from ..errors import Errors, TempErrors
-from .pipes import Pipe
+from .pipes import Tagger, _load_cfg
+from .. import util
 
 
 @component("morphologizer", assigns=["token.morph", "token.pos"])
-class Morphologizer(Pipe):
+class Morphologizer(Tagger):
 
     def __init__(self, vocab, model, **cfg):
         self.vocab = vocab
         self.model = model
+        self._rehearsal_model = None
         self.cfg = dict(sorted(cfg.items()))
-        self._class_map = self.vocab.morphology.create_class_map()  # Morphology.create_class_map() ?
+        self.cfg.setdefault("labels", {})
+        self.cfg.setdefault("morph_pos", {})
 
     @property
     def labels(self):
-        return self.vocab.morphology.tag_names
+        return tuple(self.cfg["labels"].keys())
 
-    @property
+    def add_label(self, label):
-    def tok2vec(self):
+        if not isinstance(label, str):
-        if self.model in (None, True, False):
+            raise ValueError(Errors.E187)
-            return None
+        if label in self.labels:
-        else:
+            return 0
-            return chain(self.model.get_ref("tok2vec"), list2array())
+        morph = Morphology.feats_to_dict(label)
-
+        norm_morph_pos = self.vocab.strings[self.vocab.morphology.add(morph)]
-    def __call__(self, doc):
+        pos = morph.get("POS", "")
-        features, tokvecs = self.predict([doc])
+        if norm_morph_pos not in self.cfg["labels"]:
-        self.set_annotations([doc], features, tensors=tokvecs)
+            self.cfg["labels"][norm_morph_pos] = norm_morph_pos
-        return doc
+            self.cfg["morph_pos"][norm_morph_pos] = POS_IDS[pos]
-
+        return 1
-    def pipe(self, stream, batch_size=128, n_threads=-1):
-        for docs in util.minibatch(stream, size=batch_size):
-            docs = list(docs)
-            features, tokvecs = self.predict(docs)
-            self.set_annotations(docs, features, tensors=tokvecs)
-            yield from docs
 
     def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None,
                        **kwargs):
+        for example in get_examples():
+            for i, morph in enumerate(example.token_annotation.morphs):
+                pos = example.token_annotation.get_pos(i)
+                morph = Morphology.feats_to_dict(morph)
+                norm_morph = self.vocab.strings[self.vocab.morphology.add(morph)]
+                if pos:
+                    morph["POS"] = pos
+                norm_morph_pos = self.vocab.strings[self.vocab.morphology.add(morph)]
+                if norm_morph_pos not in self.cfg["labels"]:
+                    self.cfg["labels"][norm_morph_pos] = norm_morph
+                    self.cfg["morph_pos"][norm_morph_pos] = POS_IDS[pos]
         self.set_output(len(self.labels))
         self.model.initialize()
+        link_vectors_to_models(self.vocab)
         if sgd is None:
             sgd = self.create_optimizer()
         return sgd
 
-    def predict(self, docs):
+    def set_annotations(self, docs, batch_tag_ids):
-        if not any(len(doc) for doc in docs):
-            # Handle case where there are no tokens in any docs.
-            n_labels = self.model.get_dim("nO")
-            guesses = [self.model.ops.alloc((0, n_labels)) for doc in docs]
-            tokvecs = self.model.ops.alloc((0, self.model.get_ref("tok2vec").get_dim("nO")))
-            return guesses, tokvecs
-        tokvecs = self.model.get_ref("tok2vec")(docs)
-        scores = self.model.get_ref("softmax")(tokvecs)
-        return scores, tokvecs
-
-    def set_annotations(self, docs, batch_scores, tensors=None):
         if isinstance(docs, Doc):
             docs = [docs]
         cdef Doc doc
         cdef Vocab vocab = self.vocab
-        offsets = [self._class_map.get_field_offset(field)
-                   for field in self._class_map.fields]
         for i, doc in enumerate(docs):
-            doc_scores = batch_scores[i]
+            doc_tag_ids = batch_tag_ids[i]
-            doc_guesses = scores_to_guesses(doc_scores, self.model.get_ref("softmax").attrs["nOs"])
+            if hasattr(doc_tag_ids, "get"):
-            # Convert the neuron indices into feature IDs.
+                doc_tag_ids = doc_tag_ids.get()
-            doc_feat_ids = numpy.zeros((len(doc), len(self._class_map.fields)), dtype='i')
+            for j, tag_id in enumerate(doc_tag_ids):
-            for j in range(len(doc)):
+                morph = self.labels[tag_id]
-                for k, offset in enumerate(offsets):
+                doc.c[j].morph = self.vocab.morphology.add(self.cfg["labels"][morph])
-                    if doc_guesses[j, k] == 0:
+                doc.c[j].pos = self.cfg["morph_pos"][morph]
-                        doc_feat_ids[j, k] = 0
-                    else:
-                        doc_feat_ids[j, k] = offset + doc_guesses[j, k]
-                # Get the set of feature names.
-                feats = {self._class_map.col2info[f][2] for f in doc_feat_ids[j]}
-                if "NIL" in feats:
-                    feats.remove("NIL")
-                # Now add the analysis, and set the hash.
-                doc.c[j].morph = self.vocab.morphology.add(feats)
-                if doc[j].morph.pos != 0:
-                    doc.c[j].pos = doc[j].morph.pos
 
-    def update(self, examples, drop=0., sgd=None, losses=None):
+            doc.is_morphed = True
-        if losses is not None and self.name not in losses:
-            losses[self.name] = 0.
-
-        docs = [self._get_doc(ex) for ex in examples]
-        tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
-        loss, d_tag_scores = self.get_loss(examples, tag_scores)
-        bp_tag_scores(d_tag_scores, sgd=sgd)
-
-        if losses is not None:
-            losses[self.name] += loss
 
     def get_loss(self, examples, scores):
-        guesses = []
+        scores = self.model.ops.flatten(scores)
-        for doc_scores in scores:
+        tag_index = {tag: i for i, tag in enumerate(self.labels)}
-            guesses.append(scores_to_guesses(doc_scores, self.model.get_ref("softmax").attrs["nOs"]))
-        guesses = self.model.ops.xp.vstack(guesses)
-        scores = self.model.ops.xp.vstack(scores)
-        if not isinstance(scores, numpy.ndarray):
-            scores = scores.get()
-        if not isinstance(guesses, numpy.ndarray):
-            guesses = guesses.get()
         cdef int idx = 0
-        # Do this on CPU, as we can't vectorize easily.
+        correct = numpy.zeros((scores.shape[0],), dtype="i")
-        target = numpy.zeros(scores.shape, dtype='f')
+        guesses = scores.argmax(axis=1)
-        field_sizes = self.model.get_ref("softmax").attrs["nOs"]
+        known_labels = numpy.ones((scores.shape[0], 1), dtype="f")
-        for example in examples:
+        for ex in examples:
-            doc = example.doc
+            gold = ex.gold
-            gold = example.gold
+            for i in range(len(gold.morphs)):
-            for t, features in enumerate(gold.morphology):
+                pos = gold.pos[i] if i < len(gold.pos) else ""
-                if features is None:
+                morph = gold.morphs[i]
-                    target[idx] = scores[idx]
+                feats = Morphology.feats_to_dict(morph)
+                if pos:
+                    feats["POS"] = pos
+                if len(feats) > 0:
+                    morph = self.vocab.strings[self.vocab.morphology.add(feats)]
+                if morph == "":
+                    morph = Morphology.EMPTY_MORPH
+                if morph is None:
+                    correct[idx] = guesses[idx]
+                elif morph in tag_index:
+                    correct[idx] = tag_index[morph]
                 else:
-                    gold_fields = {}
+                    correct[idx] = 0
-                    for feature in features:
+                    known_labels[idx] = 0.
-                        field = self._class_map.feat2field[feature]
-                        gold_fields[field] = self._class_map.feat2offset[feature]
-                    for field in self._class_map.fields:
-                        field_id = self._class_map.field2id[field]
-                        col_offset = self._class_map.field2col[field]
-                        if field_id in gold_fields:
-                            target[idx, col_offset + gold_fields[field_id]] = 1.
-                        else:
-                            target[idx, col_offset] = 1.
-                    #print(doc[t])
-                    #for col, info in enumerate(self._class_map.col2info):
-                    #    print(col, info, scores[idx, col], target[idx, col])
                 idx += 1
-        target = self.model.ops.asarray(target, dtype='f')
+        correct = self.model.ops.xp.array(correct, dtype="i")
-        scores = self.model.ops.asarray(scores, dtype='f')
+        d_scores = scores - to_categorical(correct, n_classes=scores.shape[1])
-        d_scores = scores - target
+        d_scores *= self.model.ops.asarray(known_labels)
         loss = (d_scores**2).sum()
-        docs = [self._get_doc(ex) for ex in examples]
+        docs = [ex.doc for ex in examples]
         d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
         return float(loss), d_scores
 
-    def use_params(self, params):
+    def to_bytes(self, exclude=tuple(), **kwargs):
-        with self.model.use_params(params):
+        serialize = {}
-            yield
+        serialize["model"] = self.model.to_bytes
+        serialize["vocab"] = self.vocab.to_bytes
+        serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
+        exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
+        return util.to_bytes(serialize, exclude)
 
-def scores_to_guesses(scores, out_sizes):
+    def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
-    xp = get_array_module(scores)
+        def load_model(b):
-    guesses = xp.zeros((scores.shape[0], len(out_sizes)), dtype='i')
+            try:
-    offset = 0
+                self.model.from_bytes(b)
-    for i, size in enumerate(out_sizes):
+            except AttributeError:
-        slice_ = scores[:, offset : offset + size]
+                raise ValueError(Errors.E149)
-        col_guesses = slice_.argmax(axis=1)
+
-        guesses[:, i] = col_guesses
+        deserialize = {
-        offset += size
+            "vocab": lambda b: self.vocab.from_bytes(b),
-    return guesses
+            "cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
+            "model": lambda b: load_model(b),
+        }
+        exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
+        util.from_bytes(bytes_data, deserialize, exclude)
+        return self
+
+    def to_disk(self, path, exclude=tuple(), **kwargs):
+        serialize = {
+            "vocab": lambda p: self.vocab.to_disk(p),
+            "model": lambda p: p.open("wb").write(self.model.to_bytes()),
+            "cfg": lambda p: srsly.write_json(p, self.cfg),
+        }
+        exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
+        util.to_disk(path, serialize, exclude)
+
+    def from_disk(self, path, exclude=tuple(), **kwargs):
+        def load_model(p):
+            with p.open("rb") as file_:
+                try:
+                    self.model.from_bytes(file_.read())
+                except AttributeError:
+                    raise ValueError(Errors.E149)
+
+        deserialize = {
+            "vocab": lambda p: self.vocab.from_disk(p),
+            "cfg": lambda p: self.cfg.update(_load_cfg(p)),
+            "model": load_model,
+        }
+        exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
+        util.from_disk(path, deserialize, exclude)
+        return self

spacy/scorer.py

@@ -81,6 +81,9 @@ class Scorer(object):
         self.labelled = PRFScore()
         self.labelled_per_dep = dict()
         self.tags = PRFScore()
+        self.pos = PRFScore()
+        self.morphs = PRFScore()
+        self.morphs_per_feat = dict()
         self.sent_starts = PRFScore()
         self.ner = PRFScore()
         self.ner_per_ents = dict()
@@ -111,6 +114,29 @@ class Scorer(object):
         """
         return self.tags.fscore * 100
 
+    @property
+    def pos_acc(self):
+        """RETURNS (float): Part-of-speech tag accuracy (coarse grained pos,
+            i.e. `Token.pos`).
+        """
+        return self.pos.fscore * 100
+
+    @property
+    def morphs_acc(self):
+       """RETURNS (float): Morph tag accuracy (morphological features,
+           i.e. `Token.morph`).
+       """
+       return self.morphs.fscore * 100
+
+    @property
+    def morphs_per_type(self):
+       """RETURNS (dict): Scores per dependency label.
+       """
+       return {
+           k: {"p": v.precision * 100, "r": v.recall * 100, "f": v.fscore * 100}
+           for k, v in self.morphs_per_feat.items()
+       }
+
     @property
     def sent_p(self):
         """RETURNS (float): F-score for identification of sentence starts.
@@ -231,6 +257,9 @@ class Scorer(object):
             "ents_f": self.ents_f,
             "ents_per_type": self.ents_per_type,
             "tags_acc": self.tags_acc,
+            "pos_acc": self.pos_acc,
+            "morphs_acc": self.morphs_acc,
+            "morphs_per_type": self.morphs_per_type,
             "sent_p": self.sent_p,
             "sent_r": self.sent_r,
             "sent_f": self.sent_f,
@@ -264,12 +293,23 @@ class Scorer(object):
         gold_deps = set()
         gold_deps_per_dep = {}
         gold_tags = set()
+        gold_pos = set()
+        gold_morphs = set()
+        gold_morphs_per_feat = {}
         gold_sent_starts = set()
         gold_ents = set(tags_to_entities(orig.entities))
-        for id_, tag, head, dep, sent_start in zip(
+        for id_, tag, pos, morph, head, dep, sent_start in zip(orig.ids, orig.tags, orig.pos, orig.morphs, orig.heads, orig.deps, orig.sent_starts):
-            orig.ids, orig.tags, orig.heads, orig.deps, orig.sent_starts
-        ):
             gold_tags.add((id_, tag))
+            gold_pos.add((id_, pos))
+            gold_morphs.add((id_, morph))
+            if morph:
+                for feat in morph.split("|"):
+                    field, values = feat.split("=")
+                    if field not in self.morphs_per_feat:
+                        self.morphs_per_feat[field] = PRFScore()
+                    if field not in gold_morphs_per_feat:
+                        gold_morphs_per_feat[field] = set()
+                    gold_morphs_per_feat[field].add((id_, feat))
             if sent_start:
                 gold_sent_starts.add(id_)
             if dep not in (None, "") and dep.lower() not in punct_labels:
@@ -282,6 +322,9 @@ class Scorer(object):
         cand_deps = set()
         cand_deps_per_dep = {}
         cand_tags = set()
+        cand_pos = set()
+        cand_morphs = set()
+        cand_morphs_per_feat = {}
         cand_sent_starts = set()
         for token in doc:
             if token.orth_.isspace():
@@ -292,6 +335,16 @@ class Scorer(object):
             else:
                 self.tokens.tp += 1
                 cand_tags.add((gold_i, token.tag_))
+                cand_pos.add((gold_i, token.pos_))
+                cand_morphs.add((gold_i, token.morph_))
+                if token.morph_:
+                    for feat in token.morph_.split("|"):
+                        field, values = feat.split("=")
+                        if field not in self.morphs_per_feat:
+                            self.morphs_per_feat[field] = PRFScore()
+                        if field not in cand_morphs_per_feat:
+                            cand_morphs_per_feat[field] = set()
+                        cand_morphs_per_feat[field].add((gold_i, feat))
                 if token.is_sent_start:
                     cand_sent_starts.add(gold_i)
             if token.dep_.lower() not in punct_labels and token.orth_.strip():
@@ -340,6 +393,10 @@ class Scorer(object):
             # Score for all ents
             self.ner.score_set(cand_ents, gold_ents)
         self.tags.score_set(cand_tags, gold_tags)
+        self.pos.score_set(cand_pos, gold_pos)
+        self.morphs.score_set(cand_morphs, gold_morphs)
+        for field in self.morphs_per_feat:
+            self.morphs_per_feat[field].score_set(cand_morphs_per_feat.get(field, set()), gold_morphs_per_feat.get(field, set()))
         self.sent_starts.score_set(cand_sent_starts, gold_sent_starts)
         self.labelled.score_set(cand_deps, gold_deps)
         for dep in self.labelled_per_dep:

spacy/tests/pipeline/test_morphologizer.py

@@ -0,0 +1,49 @@
+import pytest
+
+from spacy import util
+from spacy.lang.en import English
+from spacy.language import Language
+from spacy.tests.util import make_tempdir
+
+
+def test_label_types():
+    nlp = Language()
+    nlp.add_pipe(nlp.create_pipe("morphologizer"))
+    nlp.get_pipe("morphologizer").add_label("Feat=A")
+    with pytest.raises(ValueError):
+        nlp.get_pipe("morphologizer").add_label(9)
+
+
+TRAIN_DATA = [
+    ("I like green eggs", {"morphs": ["Feat=N", "Feat=V", "Feat=J", "Feat=N"], "pos": ["NOUN", "VERB", "ADJ", "NOUN"]}),
+    ("Eat blue ham", {"morphs": ["Feat=V", "Feat=J", "Feat=N"], "pos": ["VERB", "ADJ", "NOUN"]}),
+]
+
+
+def test_overfitting_IO():
+    # Simple test to try and quickly overfit the morphologizer - ensuring the ML models work correctly
+    nlp = English()
+    morphologizer = nlp.create_pipe("morphologizer")
+    for inst in TRAIN_DATA:
+        for morph, pos in zip(inst[1]["morphs"], inst[1]["pos"]):
+            morphologizer.add_label(morph + "|POS=" + pos)
+    nlp.add_pipe(morphologizer)
+    optimizer = nlp.begin_training()
+
+    for i in range(50):
+        losses = {}
+        nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
+    assert losses["morphologizer"] < 0.00001
+
+    # test the trained model
+    test_text = "I like blue eggs"
+    doc = nlp(test_text)
+    gold_morphs = ["Feat=N|POS=NOUN", "Feat=V|POS=VERB", "Feat=J|POS=ADJ", "Feat=N|POS=NOUN"]
+    assert gold_morphs == [t.morph_ for t in doc]
+
+    # Also test the results are still the same after IO
+    with make_tempdir() as tmp_dir:
+        nlp.to_disk(tmp_dir)
+        nlp2 = util.load_model_from_path(tmp_dir)
+        doc2 = nlp2(test_text)
+        assert gold_morphs == [t.morph_ for t in doc2]

spacy/tests/pipeline/test_textcat.py

@@ -8,6 +8,7 @@ from spacy.language import Language
 from spacy.pipeline import TextCategorizer
 from spacy.tokens import Doc
 from spacy.gold import GoldParse
+from spacy.util import fix_random_seed
 
 from ..util import make_tempdir
 from ...ml.models.defaults import default_tok2vec
@@ -82,6 +83,7 @@ def test_label_types():
 
 def test_overfitting_IO():
     # Simple test to try and quickly overfit the textcat component - ensuring the ML models work correctly
+    fix_random_seed(0)
     nlp = English()
     textcat = nlp.create_pipe("textcat")
     for _, annotations in TRAIN_DATA:

spacy/tests/test_scorer.py

@@ -5,6 +5,7 @@ from spacy.gold import Example, GoldParse
 from spacy.scorer import Scorer, ROCAUCScore
 from spacy.scorer import _roc_auc_score, _roc_curve
 from .util import get_doc
+from spacy.lang.en import English
 
 test_las_apple = [
     [
@@ -39,6 +40,43 @@ test_ner_apple = [
     ]
 ]
 
+@pytest.fixture
+def tagged_doc():
+    text = "Sarah's sister flew to Silicon Valley via London."
+    tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
+    pos = [
+        "PROPN",
+        "PART",
+        "NOUN",
+        "VERB",
+        "ADP",
+        "PROPN",
+        "PROPN",
+        "ADP",
+        "PROPN",
+        "PUNCT",
+    ]
+    morphs = [
+        "NounType=prop|Number=sing",
+        "Poss=yes",
+        "Number=sing",
+        "Tense=past|VerbForm=fin",
+        "",
+        "NounType=prop|Number=sing",
+        "NounType=prop|Number=sing",
+        "",
+        "NounType=prop|Number=sing",
+        "PunctType=peri",
+    ]
+    nlp = English()
+    doc = nlp(text)
+    for i in range(len(tags)):
+        doc[i].tag_ = tags[i]
+        doc[i].pos_ = pos[i]
+        doc[i].morph_ = morphs[i]
+    doc.is_tagged = True
+    return doc
+
 
 def test_las_per_type(en_vocab):
     # Gold and Doc are identical
@@ -139,6 +177,43 @@ def test_ner_per_type(en_vocab):
     assert results["ents_per_type"]["ORG"]["f"] == approx(66.66666)
 
 
+def test_tag_score(tagged_doc):
+    # Gold and Doc are identical
+    scorer = Scorer()
+    gold = GoldParse(
+        tagged_doc,
+        tags=[t.tag_ for t in tagged_doc],
+        pos=[t.pos_ for t in tagged_doc],
+        morphs=[t.morph_ for t in tagged_doc]
+    )
+    scorer.score((tagged_doc, gold))
+    results = scorer.scores
+
+    assert results["tags_acc"] == 100
+    assert results["pos_acc"] == 100
+    assert results["morphs_acc"] == 100
+    assert results["morphs_per_type"]["NounType"]["f"] == 100
+
+    # Gold and Doc are identical
+    scorer = Scorer()
+    tags = [t.tag_ for t in tagged_doc]
+    tags[0] = "NN"
+    pos = [t.pos_ for t in tagged_doc]
+    pos[1] = "X"
+    morphs = [t.morph_ for t in tagged_doc]
+    morphs[1] = "Number=sing"
+    morphs[2] = "Number=plur"
+    gold = GoldParse(tagged_doc, tags=tags, pos=pos, morphs=morphs)
+    scorer.score((tagged_doc, gold))
+    results = scorer.scores
+
+    assert results["tags_acc"] == 90
+    assert results["pos_acc"] == 90
+    assert results["morphs_acc"] == approx(80)
+    assert results["morphs_per_type"]["Poss"]["f"] == 0.0
+    assert results["morphs_per_type"]["Number"]["f"] == approx(72.727272)
+
+
 def test_roc_auc_score():
     # Binary classification, toy tests from scikit-learn test suite
     y_true = [0, 1]

spacy/tokens/doc.pxd

@@ -50,6 +50,7 @@ cdef class Doc:
 
     cdef public bint is_tagged
     cdef public bint is_parsed
+    cdef public bint is_morphed
 
     cdef public float sentiment