Update sentence recognizer (#5109)

* Update sentence recognizer

* rename `sentrec` to `senter`
* use `spacy.HashEmbedCNN.v1` by default
* update to follow `Tagger` modifications
* remove component methods that can be inherited from `Tagger`
* add simple initialization and overfitting pipeline tests

* Update serialization test for senter

spacy/cli/train.py

@@ -157,6 +157,8 @@ def train(
                 config_loc = default_dir / "ner_defaults.cfg"
             elif pipe == "textcat":
                 config_loc = default_dir / "textcat_defaults.cfg"
+            elif pipe == "senter":
+                config_loc = default_dir / "senter_defaults.cfg"
             else:
                 raise ValueError(f"Component {pipe} currently not supported.")
             pipe_cfg = util.load_config(config_loc, create_objects=False)
@@ -221,6 +223,8 @@ def train(
                 config_loc = default_dir / "ner_defaults.cfg"
             elif pipe == "textcat":
                 config_loc = default_dir / "textcat_defaults.cfg"
+            elif pipe == "senter":
+                config_loc = default_dir / "senter_defaults.cfg"
             else:
                 raise ValueError(f"Component {pipe} currently not supported.")
             pipe_cfg = util.load_config(config_loc, create_objects=False)
@@ -559,7 +563,7 @@ def _score_for_model(meta):
         mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
     if "textcat" in pipes:
         mean_acc.append(acc["textcat_score"])
-    if "sentrec" in pipes:
+    if "senter" in pipes:
         mean_acc.append((acc["sent_p"] + acc["sent_r"] + acc["sent_f"]) / 3)
     return sum(mean_acc) / len(mean_acc)
 
@@ -638,7 +642,7 @@ def _get_metrics(component):
         return ("tags_acc",)
     elif component == "ner":
         return ("ents_f", "ents_p", "ents_r", "ents_per_type")
-    elif component == "sentrec":
+    elif component == "senter":
         return ("sent_f", "sent_p", "sent_r")
     elif component == "textcat":
         return ("textcat_score",)
@@ -665,9 +669,9 @@ def _configure_training_output(pipeline, use_gpu, has_beam_widths):
         elif pipe == "textcat":
             row_head.extend(["Textcat Loss", "Textcat"])
             output_stats.extend(["textcat_loss", "textcat_score"])
-        elif pipe == "sentrec":
-            row_head.extend(["Sentrec Loss", "Sent P", "Sent R", "Sent F"])
-            output_stats.extend(["sentrec_loss", "sent_p", "sent_r", "sent_f"])
+        elif pipe == "senter":
+            row_head.extend(["Senter Loss", "Sent P", "Sent R", "Sent F"])
+            output_stats.extend(["senter_loss", "sent_p", "sent_r", "sent_f"])
     row_head.extend(["Token %", "CPU WPS"])
     output_stats.extend(["token_acc", "cpu_wps"])
 
@@ -693,7 +697,7 @@ def _get_progress(
     scores["ner_loss"] = losses.get("ner", 0.0)
     scores["tag_loss"] = losses.get("tagger", 0.0)
     scores["textcat_loss"] = losses.get("textcat", 0.0)
-    scores["sentrec_loss"] = losses.get("sentrec", 0.0)
+    scores["senter_loss"] = losses.get("senter", 0.0)
     scores["cpu_wps"] = cpu_wps
     scores["gpu_wps"] = gpu_wps or 0.0
     scores.update(dev_scores)

spacy/language.py

@@ -190,7 +190,7 @@ class Language(object):
             default_textcat_config,
             default_nel_config,
             default_morphologizer_config,
-            default_sentrec_config,
+            default_senter_config,
             default_tensorizer_config,
             default_tok2vec_config,
         )
@@ -202,7 +202,7 @@ class Language(object):
             "textcat": default_textcat_config(),
             "entity_linker": default_nel_config(),
             "morphologizer": default_morphologizer_config(),
-            "sentrec": default_sentrec_config(),
+            "senter": default_senter_config(),
             "tensorizer": default_tensorizer_config(),
             "tok2vec": default_tok2vec_config(),
         }
@@ -267,8 +267,8 @@ class Language(object):
         return self.get_pipe("entity_linker")
 
     @property
-    def sentrec(self):
-        return self.get_pipe("sentrec")
+    def senter(self):
+        return self.get_pipe("senter")
 
     @property
     def matcher(self):

spacy/ml/models/defaults/__init__.py

@@ -43,13 +43,13 @@ def default_ner():
     return util.load_config(loc, create_objects=True)["model"]
 
 
-def default_sentrec_config():
-    loc = Path(__file__).parent / "sentrec_defaults.cfg"
+def default_senter_config():
+    loc = Path(__file__).parent / "senter_defaults.cfg"
     return util.load_config(loc, create_objects=False)
 
 
-def default_sentrec():
-    loc = Path(__file__).parent / "sentrec_defaults.cfg"
+def default_senter():
+    loc = Path(__file__).parent / "senter_defaults.cfg"
     return util.load_config(loc, create_objects=True)["model"]
 
 

spacy/ml/models/defaults/senter_defaults.cfg

@@ -2,7 +2,7 @@
 @architectures = "spacy.Tagger.v1"
 
 [model.tok2vec]
-@architectures = "spacy.HashCharEmbedCNN.v1"
+@architectures = "spacy.HashEmbedCNN.v1"
 pretrained_vectors = null
 width = 12
 depth = 1
@@ -10,5 +10,3 @@ embed_size = 2000
 window_size = 1
 maxout_pieces = 2
 subword_features = true
-nM = 64
-nC = 8

spacy/pipeline/pipes.pyx

@@ -650,7 +650,7 @@ class Tagger(Pipe):
         return self
 
 
-@component("sentrec", assigns=["token.is_sent_start"])
+@component("senter", assigns=["token.is_sent_start"])
 class SentenceRecognizer(Tagger):
     """Pipeline component for sentence segmentation.
 
@@ -670,7 +670,7 @@ class SentenceRecognizer(Tagger):
         # are 0
         return tuple(["I", "S"])
 
-    def set_annotations(self, docs, batch_tag_ids, **_):
+    def set_annotations(self, docs, batch_tag_ids):
         if isinstance(docs, Doc):
             docs = [docs]
         cdef Doc doc
@@ -686,24 +686,6 @@ class SentenceRecognizer(Tagger):
                     else:
                         doc.c[j].sent_start = -1
 
-    def update(self, examples, drop=0., sgd=None, losses=None):
-        examples = Example.to_example_objects(examples)
-        if losses is not None and self.name not in losses:
-            losses[self.name] = 0.
-
-        if not any(len(ex.doc) if ex.doc else 0 for ex in examples):
-            # Handle cases where there are no tokens in any docs.
-            return
-        set_dropout_rate(self.model, drop)
-        tag_scores, bp_tag_scores = self.model.begin_update([ex.doc for ex in examples])
-        loss, d_tag_scores = self.get_loss(examples, tag_scores)
-        bp_tag_scores(d_tag_scores)
-        if sgd is not None:
-            self.model.finish_update(sgd)
-
-        if losses is not None:
-            losses[self.name] += loss
-
     def get_loss(self, examples, scores):
         scores = self.model.ops.flatten(scores)
         tag_index = range(len(self.labels))
@@ -732,9 +714,9 @@ class SentenceRecognizer(Tagger):
 
     def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None,
                        **kwargs):
-        cdef Vocab vocab = self.vocab
         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
@@ -742,10 +724,6 @@ class SentenceRecognizer(Tagger):
     def add_label(self, label, values=None):
         raise NotImplementedError
 
-    def use_params(self, params):
-        with self.model.use_params(params):
-            yield
-
     def to_bytes(self, exclude=tuple(), **kwargs):
         serialize = {}
         serialize["model"] = self.model.to_bytes

spacy/tests/pipeline/test_senter.py

@@ -0,0 +1,52 @@
+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("senter"))
+    with pytest.raises(NotImplementedError):
+        nlp.get_pipe("senter").add_label("A")
+
+SENT_STARTS = [0] * 14
+SENT_STARTS[0] = 1
+SENT_STARTS[5] = 1
+SENT_STARTS[9] = 1
+
+TRAIN_DATA = [
+    ("I like green eggs. Eat blue ham. I like purple eggs.", {"sent_starts": SENT_STARTS}),
+    ("She likes purple eggs. They hate ham. You like yellow eggs.", {"sent_starts": SENT_STARTS}),
+]
+
+
+def test_overfitting_IO():
+    # Simple test to try and quickly overfit the senter - ensuring the ML models work correctly
+    nlp = English()
+    senter = nlp.create_pipe("senter")
+    nlp.add_pipe(senter)
+    optimizer = nlp.begin_training()
+
+    for i in range(200):
+        losses = {}
+        nlp.update(TRAIN_DATA, sgd=optimizer, losses=losses)
+    assert losses["senter"] < 0.0001
+
+    # test the trained model
+    test_text = "I like eggs. There is ham. She likes ham."
+    doc = nlp(test_text)
+    gold_sent_starts = [0] * 12
+    gold_sent_starts[0] = 1
+    gold_sent_starts[4] = 1
+    gold_sent_starts[8] = 1
+    assert gold_sent_starts == [int(t.is_sent_start) 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_sent_starts == [int(t.is_sent_start) for t in doc2]

spacy/tests/serialize/test_serialize_pipeline.py

@@ -2,7 +2,7 @@ import pytest
 from spacy.pipeline import Tagger, DependencyParser, EntityRecognizer
 from spacy.pipeline import Tensorizer, TextCategorizer, SentenceRecognizer
 from spacy.ml.models.defaults import default_parser, default_tensorizer, default_tagger
-from spacy.ml.models.defaults import default_textcat, default_sentrec
+from spacy.ml.models.defaults import default_textcat, default_senter
 
 from ..util import make_tempdir
 
@@ -146,7 +146,7 @@ def test_serialize_pipe_exclude(en_vocab, Parser):
 
 
 def test_serialize_sentencerecognizer(en_vocab):
-    sr = SentenceRecognizer(en_vocab, default_sentrec())
+    sr = SentenceRecognizer(en_vocab, default_senter())
     sr_b = sr.to_bytes()
-    sr_d = SentenceRecognizer(en_vocab, default_sentrec()).from_bytes(sr_b)
+    sr_d = SentenceRecognizer(en_vocab, default_senter()).from_bytes(sr_b)
     assert sr.to_bytes() == sr_d.to_bytes()