Merge branch 'master' into feature/el-framework

.github/contributors/wannaphongcom.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
+[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
+object code, patch, tool, sample, graphic, specification, manual,
+documentation, or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and
+registrations, in your contribution:
+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
+    * you agree that you will not assert any moral rights in your contribution
+    against us, our licensees or transferees;
+
+    * you agree that we may register a copyright in your contribution and
+    exercise all ownership rights associated with it; and
+
+    * you agree that neither of us has any duty to consult with, obtain the
+    consent of, pay or render an accounting to the other for any use or
+    distribution of your contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable,
+non-exclusive, worldwide, no-charge, royalty-free license to:
+
+    * make, have made, use, sell, offer to sell, import, and otherwise transfer
+    your contribution in whole or in part, alone or in combination with or
+    included in any product, work or materials arising out of the project to
+    which your contribution was submitted, and
+
+    * at our option, to sublicense these same rights to third parties through
+    multiple levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective
+on the date you first submitted a contribution to us, even if your submission
+took place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+    * Each contribution that you submit is and shall be an original work of
+    authorship and you can legally grant the rights set out in this SCA;
+
+    * to the best of your knowledge, each contribution will not violate any
+    third party's copyrights, trademarks, patents, or other intellectual
+    property rights; and
+
+    * each contribution shall be in compliance with U.S. export control laws and
+    other applicable export and import laws. You agree to notify us if you
+    become aware of any circumstance which would make any of the foregoing
+    representations inaccurate in any respect. We may publicly disclose your 
+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [x] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect my
+    contributions.
+
+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                          | Entry                |
+|------------------------------- | -------------------- |
+| Name                           | Wannaphong Phatthiyaphaibun           |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           | 25-3-2019           |
+| GitHub username                | wannaphongcom               |
+| Website (optional)             |                      |

examples/training/train_textcat.py

@@ -43,7 +43,11 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
     # nlp.create_pipe works for built-ins that are registered with spaCy
     if "textcat" not in nlp.pipe_names:
         textcat = nlp.create_pipe(
-            "textcat", config={"architecture": "simple_cnn", "exclusive_classes": True}
+            "textcat",
+            config={
+                "exclusive_classes": True,
+                "architecture": "simple_cnn",
+            }
         )
         nlp.add_pipe(textcat, last=True)
     # otherwise, get it, so we can add labels to it
@@ -56,7 +60,9 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
 
     # load the IMDB dataset
     print("Loading IMDB data...")
-    (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
+    (train_texts, train_cats), (dev_texts, dev_cats) = load_data()
+    train_texts = train_texts[:n_texts]
+    train_cats = train_cats[:n_texts]
     print(
         "Using {} examples ({} training, {} evaluation)".format(
             n_texts, len(train_texts), len(dev_texts)
@@ -73,10 +79,12 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
                 textcat.model.tok2vec.from_bytes(file_.read())
         print("Training the model...")
         print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
+        batch_sizes = compounding(4.0, 32.0, 1.001)
         for i in range(n_iter):
             losses = {}
             # batch up the examples using spaCy's minibatch
-            batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
+            random.shuffle(train_data)
+            batches = minibatch(train_data, size=batch_sizes)
             for batch in batches:
                 texts, annotations = zip(*batch)
                 nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)

netlify.toml

@@ -43,8 +43,9 @@ redirects = [
     {from = "/usage/lightning-tour", to = "/usage/spacy-101#lightning-tour"},
     {from = "/usage/linguistic-features#rule-based-matching", to = "/usage/rule-based-matching"},
     {from = "/models/comparison", to = "/models"},
-    {from = "/api/#section-cython", to = "/api/cython"},
+    {from = "/api/#section-cython", to = "/api/cython", force = true},
-    {from = "/api/#cython", to = "/api/cython"},
+    {from = "/api/#cython", to = "/api/cython", force = true},
+    {from = "/api/sentencesegmenter", to="/api/sentencizer"},
     {from = "/universe", to = "/universe/project/:id", query = {id = ":id"}, force = true},
     {from = "/universe", to = "/universe/category/:category", query = {category = ":category"}, force = true},
 ]

spacy/_ml.py

@@ -81,18 +81,6 @@ def _zero_init(model):
     return model
 
 
-@layerize
-def _preprocess_doc(docs, drop=0.0):
-    keys = [doc.to_array(LOWER) for doc in docs]
-    # The dtype here matches what thinc is expecting -- which differs per
-    # platform (by int definition). This should be fixed once the problem
-    # is fixed on Thinc's side.
-    lengths = numpy.array([arr.shape[0] for arr in keys], dtype=numpy.int_)
-    keys = numpy.concatenate(keys)
-    vals = numpy.zeros(keys.shape, dtype='f')
-    return (keys, vals, lengths), None
-
-
 def with_cpu(ops, model):
     """Wrap a model that should run on CPU, transferring inputs and outputs
     as necessary."""
@@ -133,20 +121,31 @@ def _to_device(ops, X):
         return ops.asarray(X)
 
 
-@layerize
+class extract_ngrams(Model):
-def _preprocess_doc_bigrams(docs, drop=0.0):
+    def __init__(self, ngram_size, attr=LOWER):
-    unigrams = [doc.to_array(LOWER) for doc in docs]
+        Model.__init__(self)
-    ops = Model.ops
+        self.ngram_size = ngram_size
-    bigrams = [ops.ngrams(2, doc_unis) for doc_unis in unigrams]
+        self.attr = attr
-    keys = [ops.xp.concatenate(feats) for feats in zip(unigrams, bigrams)]
+
-    keys, vals = zip(*[ops.xp.unique(k, return_counts=True) for k in keys])
+    def begin_update(self, docs, drop=0.0):
-    # The dtype here matches what thinc is expecting -- which differs per
+        batch_keys = []
-    # platform (by int definition). This should be fixed once the problem
+        batch_vals = []
-    # is fixed on Thinc's side.
+        for doc in docs:
-    lengths = ops.asarray([arr.shape[0] for arr in keys], dtype=numpy.int_)
+            unigrams = doc.to_array([self.attr])
-    keys = ops.xp.concatenate(keys)
+            ngrams = [unigrams]
-    vals = ops.asarray(ops.xp.concatenate(vals), dtype="f")
+            for n in range(2, self.ngram_size + 1):
-    return (keys, vals, lengths), None
+                ngrams.append(self.ops.ngrams(n, unigrams))
+            keys = self.ops.xp.concatenate(ngrams)
+            keys, vals = self.ops.xp.unique(keys, return_counts=True)
+            batch_keys.append(keys)
+            batch_vals.append(vals)
+        # The dtype here matches what thinc is expecting -- which differs per
+        # platform (by int definition). This should be fixed once the problem
+        # is fixed on Thinc's side.
+        lengths = self.ops.asarray([arr.shape[0] for arr in batch_keys], dtype=numpy.int_)
+        batch_keys = self.ops.xp.concatenate(batch_keys)
+        batch_vals = self.ops.asarray(self.ops.xp.concatenate(batch_vals), dtype="f")
+        return (batch_keys, batch_vals, lengths), None
 
 
 @describe.on_data(
@@ -486,16 +485,6 @@ def zero_init(model):
     return model
 
 
-@layerize
-def preprocess_doc(docs, drop=0.0):
-    keys = [doc.to_array([LOWER]) for doc in docs]
-    ops = Model.ops
-    lengths = ops.asarray([arr.shape[0] for arr in keys])
-    keys = ops.xp.concatenate(keys)
-    vals = ops.allocate(keys.shape[0]) + 1
-    return (keys, vals, lengths), None
-
-
 def getitem(i):
     def getitem_fwd(X, drop=0.0):
         return X[i], None
@@ -602,10 +591,8 @@ def build_text_classifier(nr_class, width=64, **cfg):
             >> zero_init(Affine(nr_class, width, drop_factor=0.0))
         )
 
-        linear_model = (
+        linear_model = build_bow_text_classifier(
-            _preprocess_doc
+            nr_class, ngram_size=cfg.get("ngram_size", 1), exclusive_classes=False)
-            >> with_cpu(Model.ops, LinearModel(nr_class))
-        )
         if cfg.get('exclusive_classes'):
             output_layer = Softmax(nr_class, nr_class * 2)
         else:
@@ -623,6 +610,33 @@ def build_text_classifier(nr_class, width=64, **cfg):
     return model
 
 
+def build_bow_text_classifier(nr_class, ngram_size=1, exclusive_classes=False,
+        no_output_layer=False, **cfg):
+    with Model.define_operators({">>": chain}):
+        model = (
+            extract_ngrams(ngram_size, attr=ORTH) 
+            >> with_cpu(Model.ops,
+                LinearModel(nr_class)
+            )
+        )
+        if not no_output_layer:
+            model = model >> (cpu_softmax if exclusive_classes else logistic)
+    model.nO = nr_class
+    return model
+
+
+@layerize
+def cpu_softmax(X, drop=0.):
+    ops = NumpyOps()
+
+    Y = ops.softmax(X)
+
+    def cpu_softmax_backward(dY, sgd=None):
+        return dY
+
+    return ops.softmax(X), cpu_softmax_backward
+
+
 def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False, **cfg):
     """
     Build a simple CNN text classifier, given a token-to-vector model as inputs.

spacy/about.py

@@ -4,7 +4,7 @@
 # fmt: off
 
 __title__ = "spacy"
-__version__ = "2.1.2"
+__version__ = "2.1.3"
 __summary__ = "Industrial-strength Natural Language Processing (NLP) with Python and Cython"
 __uri__ = "https://spacy.io"
 __author__ = "Explosion AI"

spacy/lang/th/tag_map.py

@@ -1,7 +1,7 @@
 # encoding: utf8
 from __future__ import unicode_literals
 
-from ...symbols import POS, NOUN, PRON, ADJ, ADV, INTJ, PROPN, DET, NUM, AUX
+from ...symbols import POS, NOUN, PRON, ADJ, ADV, INTJ, PROPN, DET, NUM, AUX,VERB
 from ...symbols import ADP, CCONJ, PART, PUNCT, SPACE, SCONJ
 
 # Source: Korakot Chaovavanich
@@ -16,6 +16,9 @@ TAG_MAP = {
     "CMTR": {POS: NOUN},
     "CFQC": {POS: NOUN},
     "CVBL": {POS: NOUN},
+    # VERB
+    "VACT":{POS:VERB},
+    "VSTA":{POS:VERB},
     # PRON
     "PRON": {POS: PRON},
     "NPRP": {POS: PRON},
@@ -78,6 +81,7 @@ TAG_MAP = {
     "EAFF": {POS: PART},
     "AITT": {POS: PART},
     "NEG": {POS: PART},
+    "EITT": {POS: PART},
     # PUNCT
     "PUNCT": {POS: PUNCT},
     "PUNC": {POS: PUNCT},

spacy/language.py

@@ -15,7 +15,7 @@ from .tokenizer import Tokenizer
 from .vocab import Vocab
 from .lemmatizer import Lemmatizer
 from .pipeline import DependencyParser, Tensorizer, Tagger, EntityRecognizer, EntityLinker
-from .pipeline import SimilarityHook, TextCategorizer, SentenceSegmenter
+from .pipeline import SimilarityHook, TextCategorizer, Sentencizer
 from .pipeline import merge_noun_chunks, merge_entities, merge_subtokens
 from .pipeline import EntityRuler
 from .compat import izip, basestring_
@@ -120,7 +120,7 @@ class Language(object):
         "entity_linker": lambda nlp, **cfg: EntityLinker(nlp.vocab, **cfg),
         "similarity": lambda nlp, **cfg: SimilarityHook(nlp.vocab, **cfg),
         "textcat": lambda nlp, **cfg: TextCategorizer(nlp.vocab, **cfg),
-        "sentencizer": lambda nlp, **cfg: SentenceSegmenter(nlp.vocab, **cfg),
+        "sentencizer": lambda nlp, **cfg: Sentencizer(**cfg),
         "merge_noun_chunks": lambda nlp, **cfg: merge_noun_chunks,
         "merge_entities": lambda nlp, **cfg: merge_entities,
         "merge_subtokens": lambda nlp, **cfg: merge_subtokens,

spacy/pipeline/__init__.py

@@ -2,7 +2,7 @@
 from __future__ import unicode_literals
 
 from .pipes import Tagger, DependencyParser, EntityRecognizer, EntityLinker
-from .pipes import TextCategorizer, Tensorizer, Pipe
+from .pipes import TextCategorizer, Tensorizer, Pipe, Sentencizer
 from .entityruler import EntityRuler
 from .hooks import SentenceSegmenter, SimilarityHook
 from .functions import merge_entities, merge_noun_chunks, merge_subtokens
@@ -16,6 +16,7 @@ __all__ = [
     "Tensorizer",
     "Pipe",
     "EntityRuler",
+    "Sentencizer",
     "SentenceSegmenter",
     "SimilarityHook",
     "merge_entities",

spacy/pipeline/entityruler.py

@@ -191,7 +191,7 @@ class EntityRuler(object):
         **kwargs: Other config paramters, mostly for consistency.
         RETURNS (EntityRuler): The loaded entity ruler.
 
-        DOCS: https://spacy.io/api/entityruler
+        DOCS: https://spacy.io/api/entityruler#to_disk
         """
         path = ensure_path(path)
         path = path.with_suffix(".jsonl")

spacy/pipeline/hooks.py

@@ -15,8 +15,6 @@ class SentenceSegmenter(object):
     initialization, or assign a new strategy to the .strategy attribute.
     Sentence detection strategies should be generators that take `Doc` objects
     and yield `Span` objects for each sentence.
-
-    DOCS: https://spacy.io/api/sentencesegmenter
     """
 
     name = "sentencizer"
@@ -35,12 +33,12 @@ class SentenceSegmenter(object):
     def split_on_punct(doc):
         start = 0
         seen_period = False
-        for i, word in enumerate(doc):
+        for i, token in enumerate(doc):
-            if seen_period and not word.is_punct:
+            if seen_period and not token.is_punct:
-                yield doc[start : word.i]
+                yield doc[start : token.i]
-                start = word.i
+                start = token.i
                 seen_period = False
-            elif word.text in [".", "!", "?"]:
+            elif token.text in [".", "!", "?"]:
                 seen_period = True
         if start < len(doc):
             yield doc[start : len(doc)]

spacy/pipeline/pipes.pyx

@@ -25,6 +25,7 @@ from ..attrs import POS, ID
 from ..parts_of_speech import X
 from .._ml import Tok2Vec, build_tagger_model
 from .._ml import build_text_classifier, build_simple_cnn_text_classifier
+from .._ml import build_bow_text_classifier
 from .._ml import link_vectors_to_models, zero_init, flatten
 from .._ml import masked_language_model, create_default_optimizer
 from ..errors import Errors, TempErrors
@@ -876,6 +877,8 @@ class TextCategorizer(Pipe):
         if cfg.get("architecture") == "simple_cnn":
             tok2vec = Tok2Vec(token_vector_width, embed_size, **cfg)
             return build_simple_cnn_text_classifier(tok2vec, nr_class, **cfg)
+        elif cfg.get("architecture") == "bow":
+            return build_bow_text_classifier(nr_class, **cfg)
         else:
             return build_text_classifier(nr_class, **cfg)
 
@@ -1107,4 +1110,90 @@ class EntityLinker(Pipe):
         pass
 
 
-__all__ = ["Tagger", "DependencyParser", "EntityRecognizer", "Tensorizer", "TextCategorizer", "EntityLinker"]
+class Sentencizer(object):
+    """Segment the Doc into sentences using a rule-based strategy.
+
+    DOCS: https://spacy.io/api/sentencizer
+    """
+
+    name = "sentencizer"
+    default_punct_chars = [".", "!", "?"]
+
+    def __init__(self, punct_chars=None, **kwargs):
+        """Initialize the sentencizer.
+
+        punct_chars (list): Punctuation characters to split on. Will be
+            serialized with the nlp object.
+        RETURNS (Sentencizer): The sentencizer component.
+
+        DOCS: https://spacy.io/api/sentencizer#init
+        """
+        self.punct_chars = punct_chars or self.default_punct_chars
+
+    def __call__(self, doc):
+        """Apply the sentencizer to a Doc and set Token.is_sent_start.
+
+        doc (Doc): The document to process.
+        RETURNS (Doc): The processed Doc.
+
+        DOCS: https://spacy.io/api/sentencizer#call
+        """
+        start = 0
+        seen_period = False
+        for i, token in enumerate(doc):
+            is_in_punct_chars = token.text in self.punct_chars
+            token.is_sent_start = i == 0
+            if seen_period and not token.is_punct and not is_in_punct_chars:
+                doc[start].is_sent_start = True
+                start = token.i
+                seen_period = False
+            elif is_in_punct_chars:
+                seen_period = True
+        if start < len(doc):
+            doc[start].is_sent_start = True
+        return doc
+
+    def to_bytes(self, **kwargs):
+        """Serialize the sentencizer to a bytestring.
+
+        RETURNS (bytes): The serialized object.
+
+        DOCS: https://spacy.io/api/sentencizer#to_bytes
+        """
+        return srsly.msgpack_dumps({"punct_chars": self.punct_chars})
+
+    def from_bytes(self, bytes_data, **kwargs):
+        """Load the sentencizer from a bytestring.
+
+        bytes_data (bytes): The data to load.
+        returns (Sentencizer): The loaded object.
+
+        DOCS: https://spacy.io/api/sentencizer#from_bytes
+        """
+        cfg = srsly.msgpack_loads(bytes_data)
+        self.punct_chars = cfg.get("punct_chars", self.default_punct_chars)
+        return self
+
+    def to_disk(self, path, exclude=tuple(), **kwargs):
+        """Serialize the sentencizer to disk.
+
+        DOCS: https://spacy.io/api/sentencizer#to_disk
+        """
+        path = util.ensure_path(path)
+        path = path.with_suffix(".json")
+        srsly.write_json(path, {"punct_chars": self.punct_chars})
+
+
+    def from_disk(self, path, exclude=tuple(), **kwargs):
+        """Load the sentencizer from disk.
+
+        DOCS: https://spacy.io/api/sentencizer#from_disk
+        """
+        path = util.ensure_path(path)
+        path = path.with_suffix(".json")
+        cfg = srsly.read_json(path)
+        self.punct_chars = cfg.get("punct_chars", self.default_punct_chars)
+        return self
+
+      
+__all__ = ["Tagger", "DependencyParser", "EntityRecognizer", "Tensorizer", "TextCategorizer", "EntityLinker", "Sentencizer"]

spacy/syntax/nn_parser.pyx

@@ -574,11 +574,12 @@ cdef class Parser:
         cfg.setdefault('min_action_freq', 30)
         actions = self.moves.get_actions(gold_parses=get_gold_tuples(),
                                          min_freq=cfg.get('min_action_freq', 30))
-        previous_labels = dict(self.moves.labels)
+        for action, labels in self.moves.labels.items():
+            actions.setdefault(action, {})
+            for label, freq in labels.items():
+                if label not in actions[action]:
+                    actions[action][label] = freq
         self.moves.initialize_actions(actions)
-        for action, label_freqs in previous_labels.items():
-            for label in label_freqs:
-                self.moves.add_action(action, label)
         cfg.setdefault('token_vector_width', 96)
         if self.model is True:
             self.model, cfg = self.Model(self.moves.n_moves, **cfg)

spacy/tests/parser/test_add_label.py

@@ -8,7 +8,8 @@ from spacy.attrs import NORM
 from spacy.gold import GoldParse
 from spacy.vocab import Vocab
 from spacy.tokens import Doc
-from spacy.pipeline import DependencyParser
+from spacy.pipeline import DependencyParser, EntityRecognizer
+from spacy.util import fix_random_seed
 
 
 @pytest.fixture
@@ -19,18 +20,6 @@ def vocab():
 @pytest.fixture
 def parser(vocab):
     parser = DependencyParser(vocab)
-    parser.cfg["token_vector_width"] = 8
-    parser.cfg["hidden_width"] = 30
-    parser.cfg["hist_size"] = 0
-    parser.add_label("left")
-    parser.begin_training([], **parser.cfg)
-    sgd = Adam(NumpyOps(), 0.001)
-
-    for i in range(10):
-        losses = {}
-        doc = Doc(vocab, words=["a", "b", "c", "d"])
-        gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
-        parser.update([doc], [gold], sgd=sgd, losses=losses)
     return parser
 
 
@@ -38,25 +27,23 @@ def test_init_parser(parser):
     pass
 
 
-# TODO: This is flakey, because it depends on what the parser first learns.
+def _train_parser(parser):
-# TODO: This now seems to be implicated in segfaults. Not sure what's up!
+    fix_random_seed(1)
-@pytest.mark.skip
+    parser.add_label("left")
+    parser.begin_training([], **parser.cfg)
+    sgd = Adam(NumpyOps(), 0.001)
+
+    for i in range(5):
+        losses = {}
+        doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
+        gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
+        parser.update([doc], [gold], sgd=sgd, losses=losses)
+    return parser
+
+
 def test_add_label(parser):
-    doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
+    parser = _train_parser(parser)
-    doc = parser(doc)
-    assert doc[0].head.i == 1
-    assert doc[0].dep_ == "left"
-    assert doc[1].head.i == 1
-    assert doc[2].head.i == 3
-    assert doc[2].head.i == 3
     parser.add_label("right")
-    doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
-    doc = parser(doc)
-    assert doc[0].head.i == 1
-    assert doc[0].dep_ == "left"
-    assert doc[1].head.i == 1
-    assert doc[2].head.i == 3
-    assert doc[2].head.i == 3
     sgd = Adam(NumpyOps(), 0.001)
     for i in range(10):
         losses = {}
@@ -69,3 +56,15 @@ def test_add_label(parser):
     doc = parser(doc)
     assert doc[0].dep_ == "right"
     assert doc[2].dep_ == "left"
+
+
+def test_add_label_deserializes_correctly():
+    ner1 = EntityRecognizer(Vocab())
+    ner1.add_label("C")
+    ner1.add_label("B")
+    ner1.add_label("A")
+    ner1.begin_training([])
+    ner2 = EntityRecognizer(Vocab()).from_bytes(ner1.to_bytes())
+    assert ner1.moves.n_moves == ner2.moves.n_moves
+    for i in range(ner1.moves.n_moves):
+        assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)

spacy/tests/pipeline/test_sentencizer.py

@@ -0,0 +1,87 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import pytest
+from spacy.pipeline import Sentencizer
+from spacy.tokens import Doc
+
+
+def test_sentencizer(en_vocab):
+    doc = Doc(en_vocab, words=["Hello", "!", "This", "is", "a", "test", "."])
+    sentencizer = Sentencizer()
+    doc = sentencizer(doc)
+    assert doc.is_sentenced
+    sent_starts = [t.is_sent_start for t in doc]
+    assert sent_starts == [True, False, True, False, False, False, False]
+    assert len(list(doc.sents)) == 2
+
+
+@pytest.mark.parametrize(
+    "words,sent_starts,n_sents",
+    [
+        # The expected result here is that the duplicate punctuation gets merged
+        # onto the same sentence and no one-token sentence is created for them.
+        (
+            ["Hello", "!", ".", "Test", ".", ".", "ok"],
+            [True, False, False, True, False, False, True],
+            3,
+        ),
+        # We also want to make sure ¡ and ¿ aren't treated as sentence end
+        # markers, even though they're punctuation
+        (
+            ["¡", "Buen", "día", "!", "Hola", ",", "¿", "qué", "tal", "?"],
+            [True, False, False, False, True, False, False, False, False, False],
+            2,
+        ),
+        # The Token.is_punct check ensures that quotes are handled as well
+        (
+            ['"', "Nice", "!", '"', "I", "am", "happy", "."],
+            [True, False, False, False, True, False, False, False],
+            2,
+        ),
+    ],
+)
+def test_sentencizer_complex(en_vocab, words, sent_starts, n_sents):
+    doc = Doc(en_vocab, words=words)
+    sentencizer = Sentencizer()
+    doc = sentencizer(doc)
+    assert doc.is_sentenced
+    assert [t.is_sent_start for t in doc] == sent_starts
+    assert len(list(doc.sents)) == n_sents
+
+
+@pytest.mark.parametrize(
+    "punct_chars,words,sent_starts,n_sents",
+    [
+        (
+            ["~", "?"],
+            ["Hello", "world", "~", "A", ".", "B", "."],
+            [True, False, False, True, False, False, False],
+            2,
+        ),
+        # Even thought it's not common, the punct_chars should be able to
+        # handle any tokens
+        (
+            [".", "ö"],
+            ["Hello", ".", "Test", "ö", "Ok", "."],
+            [True, False, True, False, True, False],
+            3,
+        ),
+    ],
+)
+def test_sentencizer_custom_punct(en_vocab, punct_chars, words, sent_starts, n_sents):
+    doc = Doc(en_vocab, words=words)
+    sentencizer = Sentencizer(punct_chars=punct_chars)
+    doc = sentencizer(doc)
+    assert doc.is_sentenced
+    assert [t.is_sent_start for t in doc] == sent_starts
+    assert len(list(doc.sents)) == n_sents
+
+
+def test_sentencizer_serialize_bytes(en_vocab):
+    punct_chars = [".", "~", "+"]
+    sentencizer = Sentencizer(punct_chars=punct_chars)
+    assert sentencizer.punct_chars == punct_chars
+    bytes_data = sentencizer.to_bytes()
+    new_sentencizer = Sentencizer().from_bytes(bytes_data)
+    assert new_sentencizer.punct_chars == punct_chars

spacy/tests/regression/test_issue3468.py

@@ -0,0 +1,22 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import pytest
+from spacy.lang.en import English
+from spacy.tokens import Doc
+
+
+def test_issue3468():
+    """Test that sentence boundaries are set correctly so Doc.is_sentenced can
+    be restored after serialization."""
+    nlp = English()
+    nlp.add_pipe(nlp.create_pipe("sentencizer"))
+    doc = nlp("Hello world")
+    assert doc[0].is_sent_start
+    assert doc.is_sentenced
+    assert len(list(doc.sents)) == 1
+    doc_bytes = doc.to_bytes()
+    new_doc = Doc(nlp.vocab).from_bytes(doc_bytes)
+    assert new_doc[0].is_sent_start
+    assert new_doc.is_sentenced
+    assert len(list(new_doc.sents)) == 1

spacy/tokens/doc.pyx

@@ -230,7 +230,7 @@ cdef class Doc:
         defined as having at least one of the following:
 
         a) An entry "sents" in doc.user_hooks";
-        b) sent.is_parsed is set to True;
+        b) Doc.is_parsed is set to True;
         c) At least one token other than the first where sent_start is not None.
         """
         if "sents" in self.user_hooks:

spacy/tokens/token.pyx

@@ -441,6 +441,7 @@ cdef class Token:
 
     property sent_start:
         def __get__(self):
+            """Deprecated: use Token.is_sent_start instead."""
             # Raising a deprecation warning here causes errors for autocomplete
             # Handle broken backwards compatibility case: doc[0].sent_start
             # was False.

website/docs/api/sentencesegmenter.md

@@ -1,78 +0,0 @@
----
-title: SentenceSegmenter
-tag: class
-source: spacy/pipeline/hooks.py
----
-
-A simple spaCy hook, to allow custom sentence boundary detection logic that
-doesn't require the dependency parse. By default, sentence segmentation is
-performed by the [`DependencyParser`](/api/dependencyparser), so the
-`SentenceSegmenter` lets you implement a simpler, rule-based strategy that
-doesn't require a statistical model to be loaded. The component is also
-available via the string name `"sentencizer"`. After initialization, it is
-typically added to the processing pipeline using
-[`nlp.add_pipe`](/api/language#add_pipe).
-
-## SentenceSegmenter.\_\_init\_\_ {#init tag="method"}
-
-Initialize the sentence segmenter. To change the sentence boundary detection
-strategy, pass a generator function `strategy` on initialization, or assign a
-new strategy to the `.strategy` attribute. Sentence detection strategies should
-be generators that take `Doc` objects and yield `Span` objects for each
-sentence.
-
-> #### Example
->
-> ```python
-> # Construction via create_pipe
-> sentencizer = nlp.create_pipe("sentencizer")
->
-> # Construction from class
-> from spacy.pipeline import SentenceSegmenter
-> sentencizer = SentenceSegmenter(nlp.vocab)
-> ```
-
-| Name        | Type                | Description                                                 |
-| ----------- | ------------------- | ----------------------------------------------------------- |
-| `vocab`     | `Vocab`             | The shared vocabulary.                                      |
-| `strategy`  | unicode / callable  | The segmentation strategy to use. Defaults to `"on_punct"`. |
-| **RETURNS** | `SentenceSegmenter` | The newly constructed object.                               |
-
-## SentenceSegmenter.\_\_call\_\_ {#call tag="method"}
-
-Apply the sentence segmenter on a `Doc`. Typically, this happens automatically
-after the component has been added to the pipeline using
-[`nlp.add_pipe`](/api/language#add_pipe).
-
-> #### Example
->
-> ```python
-> from spacy.lang.en import English
->
-> nlp = English()
-> sentencizer = nlp.create_pipe("sentencizer")
-> nlp.add_pipe(sentencizer)
-> doc = nlp(u"This is a sentence. This is another sentence.")
-> assert list(doc.sents) == 2
-> ```
-
-| Name        | Type  | Description                                                  |
-| ----------- | ----- | ------------------------------------------------------------ |
-| `doc`       | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
-| **RETURNS** | `Doc` | The modified `Doc` with added sentence boundaries.           |
-
-## SentenceSegmenter.split_on_punct {#split_on_punct tag="staticmethod"}
-
-Split the `Doc` on punctuation characters `.`, `!` and `?`. This is the default
-strategy used by the `SentenceSegmenter.`
-
-| Name       | Type   | Description                    |
-| ---------- | ------ | ------------------------------ |
-| `doc`      | `Doc`  | The `Doc` object to process.   |
-| **YIELDS** | `Span` | The sentences in the document. |
-
-## Attributes {#attributes}
-
-| Name       | Type     | Description                                                         |
-| ---------- | -------- | ------------------------------------------------------------------- |
-| `strategy` | callable | The segmentation strategy. Can be overwritten after initialization. |

website/docs/api/sentencizer.md

@@ -0,0 +1,136 @@
+---
+title: Sentencizer
+tag: class
+source: spacy/pipeline/pipes.pyx
+---
+
+A simple pipeline component, to allow custom sentence boundary detection logic
+that doesn't require the dependency parse. By default, sentence segmentation is
+performed by the [`DependencyParser`](/api/dependencyparser), so the
+`Sentencizer` lets you implement a simpler, rule-based strategy that doesn't
+require a statistical model to be loaded. The component is also available via
+the string name `"sentencizer"`. After initialization, it is typically added to
+the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
+
+<Infobox title="Important note" variant="warning">
+
+Compared to the previous `SentenceSegmenter` class, the `Sentencizer` component
+doesn't add a hook to `doc.user_hooks["sents"]`. Instead, it iterates over the
+tokens in the `Doc` and sets the `Token.is_sent_start` property. The
+`SentenceSegmenter` is still available if you import it directly:
+
+```python
+from spacy.pipeline import SentenceSegmenter
+```
+
+</Infobox>
+
+## Sentencizer.\_\_init\_\_ {#init tag="method"}
+
+Initialize the sentencizer.
+
+> #### Example
+>
+> ```python
+> # Construction via create_pipe
+> sentencizer = nlp.create_pipe("sentencizer")
+>
+> # Construction from class
+> from spacy.pipeline import Sentencizer
+> sentencizer = Sentencizer()
+> ```
+
+| Name          | Type          | Description                                                                                            |
+| ------------- | ------------- | ------------------------------------------------------------------------------------------------------ |
+| `punct_chars` | list          | Optional custom list of punctuation characters that mark sentence ends. Defaults to `[".", "!", "?"].` |
+| **RETURNS**   | `Sentencizer` | The newly constructed object.                                                                          |
+
+## Sentencizer.\_\_call\_\_ {#call tag="method"}
+
+Apply the sentencizer on a `Doc`. Typically, this happens automatically after
+the component has been added to the pipeline using
+[`nlp.add_pipe`](/api/language#add_pipe).
+
+> #### Example
+>
+> ```python
+> from spacy.lang.en import English
+>
+> nlp = English()
+> sentencizer = nlp.create_pipe("sentencizer")
+> nlp.add_pipe(sentencizer)
+> doc = nlp(u"This is a sentence. This is another sentence.")
+> assert list(doc.sents) == 2
+> ```
+
+| Name        | Type  | Description                                                  |
+| ----------- | ----- | ------------------------------------------------------------ |
+| `doc`       | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
+| **RETURNS** | `Doc` | The modified `Doc` with added sentence boundaries.           |
+
+## Sentencizer.to_disk {#to_disk tag="method"}
+
+Save the sentencizer settings (punctuation characters) a directory. Will create
+a file `sentencizer.json`. This also happens automatically when you save an
+`nlp` object with a sentencizer added to its pipeline.
+
+> #### Example
+>
+> ```python
+> sentencizer = Sentencizer(punct_chars=[".", "?", "!", "。"])
+> sentencizer.to_disk("/path/to/sentencizer.jsonl")
+> ```
+
+| Name   | Type             | Description                                                                                                      |
+| ------ | ---------------- | ---------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a file, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+
+## Sentencizer.from_disk {#from_disk tag="method"}
+
+Load the sentencizer settings from a file. Expects a JSON file. This also
+happens automatically when you load an `nlp` object or model with a sentencizer
+added to its pipeline.
+
+> #### Example
+>
+> ```python
+> sentencizer = Sentencizer()
+> sentencizer.from_disk("/path/to/sentencizer.json")
+> ```
+
+| Name        | Type             | Description                                                                |
+| ----------- | ---------------- | -------------------------------------------------------------------------- |
+| `path`      | unicode / `Path` | A path to a JSON file. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `Sentencizer`    | The modified `Sentencizer` object.                                         |
+
+## Sentencizer.to_bytes {#to_bytes tag="method"}
+
+Serialize the sentencizer settings to a bytestring.
+
+> #### Example
+>
+> ```python
+> sentencizer = Sentencizer(punct_chars=[".", "?", "!", "。"])
+> sentencizer_bytes = sentencizer.to_bytes()
+> ```
+
+| Name        | Type  | Description          |
+| ----------- | ----- | -------------------- |
+| **RETURNS** | bytes | The serialized data. |
+
+## Sentencizer.from_bytes {#from_bytes tag="method"}
+
+Load the pipe from a bytestring. Modifies the object in place and returns it.
+
+> #### Example
+>
+> ```python
+> sentencizer_bytes = sentencizer.to_bytes()
+> sentencizer = Sentencizer()
+> sentencizer.from_bytes(sentencizer_bytes)
+> ```
+
+| Name         | Type          | Description                        |
+| ------------ | ------------- | ---------------------------------- |
+| `bytes_data` | bytes         | The bytestring to load.            |
+| **RETURNS**  | `Sentencizer` | The modified `Sentencizer` object. |

website/docs/api/textcategorizer.md

@@ -56,10 +56,11 @@ of problems. To handle a wider variety of problems, the `TextCategorizer` object
 allows configuration of its model architecture, using the `architecture` keyword
 argument.
 
-| Name           | Description                                                                                                                                              |
+| Name           | Description                                                                                                                                                                                                                                                                                                                                                                                                      |
-| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `"ensemble"`   | **Default:** Stacked ensemble of a unigram bag-of-words model and a neural network model. The neural network uses a CNN with mean pooling and attention. |
+| `"ensemble"`   | **Default:** Stacked ensemble of a bag-of-words model and a neural network model. The neural network uses a CNN with mean pooling and attention. The "ngram_size" and "attr" arguments can be used to configure the feature extraction for the bag-of-words model.                                                                                                                                               |
-| `"simple_cnn"` | A neural network model where token vectors are calculated using a CNN. The vectors are mean pooled and used as features in a feed-forward network.       |
+| `"simple_cnn"` | A neural network model where token vectors are calculated using a CNN. The vectors are mean pooled and used as features in a feed-forward network. This architecture is usually less accurate than the ensemble, but runs faster.                                                                                                                                                                                |
+| `"bow"`        | An ngram "bag-of-words" model. This architecture should run much faster than the others, but may not be as accurate, especially if texts are short. The features extracted can be controlled using the keyword arguments `ngram_size` and `attr`. For instance, `ngram_size=3` and `attr="lower"` would give lower-cased unigram, trigram and bigram features. 2, 3 or 4 are usually good choices of ngram size. |
 
 ## TextCategorizer.\_\_call\_\_ {#call tag="method"}
 

website/docs/usage/101/_architecture.md

@@ -25,21 +25,21 @@ an **annotated document**. It also orchestrates training and serialization.
 
 ### Processing pipeline {#architecture-pipeline}
 
-| Name                                          | Description                                                                                                                   |
+| Name                                        | Description                                                                                                                   |
-| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| [`Language`](/api/language)                   | A text-processing pipeline. Usually you'll load this once per process as `nlp` and pass the instance around your application. |
+| [`Language`](/api/language)                 | A text-processing pipeline. Usually you'll load this once per process as `nlp` and pass the instance around your application. |
-| [`Tokenizer`](/api/tokenizer)                 | Segment text, and create `Doc` objects with the discovered segment boundaries.                                                |
+| [`Tokenizer`](/api/tokenizer)               | Segment text, and create `Doc` objects with the discovered segment boundaries.                                                |
-| [`Lemmatizer`](/api/lemmatizer)               | Determine the base forms of words.                                                                                            |
+| [`Lemmatizer`](/api/lemmatizer)             | Determine the base forms of words.                                                                                            |
-| `Morphology`                                  | Assign linguistic features like lemmas, noun case, verb tense etc. based on the word and its part-of-speech tag.              |
+| `Morphology`                                | Assign linguistic features like lemmas, noun case, verb tense etc. based on the word and its part-of-speech tag.              |
-| [`Tagger`](/api/tagger)                       | Annotate part-of-speech tags on `Doc` objects.                                                                                |
+| [`Tagger`](/api/tagger)                     | Annotate part-of-speech tags on `Doc` objects.                                                                                |
-| [`DependencyParser`](/api/dependencyparser)   | Annotate syntactic dependencies on `Doc` objects.                                                                             |
+| [`DependencyParser`](/api/dependencyparser) | Annotate syntactic dependencies on `Doc` objects.                                                                             |
-| [`EntityRecognizer`](/api/entityrecognizer)   | Annotate named entities, e.g. persons or products, on `Doc` objects.                                                          |
+| [`EntityRecognizer`](/api/entityrecognizer) | Annotate named entities, e.g. persons or products, on `Doc` objects.                                                          |
-| [`TextCategorizer`](/api/textcategorizer)     | Assign categories or labels to `Doc` objects.                                                                                 |
+| [`TextCategorizer`](/api/textcategorizer)   | Assign categories or labels to `Doc` objects.                                                                                 |
-| [`Matcher`](/api/matcher)                     | Match sequences of tokens, based on pattern rules, similar to regular expressions.                                            |
+| [`Matcher`](/api/matcher)                   | Match sequences of tokens, based on pattern rules, similar to regular expressions.                                            |
-| [`PhraseMatcher`](/api/phrasematcher)         | Match sequences of tokens based on phrases.                                                                                   |
+| [`PhraseMatcher`](/api/phrasematcher)       | Match sequences of tokens based on phrases.                                                                                   |
-| [`EntityRuler`](/api/entityruler)             | Add entity spans to the `Doc` using token-based rules or exact phrase matches.                                                |
+| [`EntityRuler`](/api/entityruler)           | Add entity spans to the `Doc` using token-based rules or exact phrase matches.                                                |
-| [`SentenceSegmenter`](/api/sentencesegmenter) | Implement custom sentence boundary detection logic that doesn't require the dependency parse.                                 |
+| [`Sentencizer`](/api/sentencizer)           | Implement custom sentence boundary detection logic that doesn't require the dependency parse.                                 |
-| [Other functions](/api/pipeline-functions)    | Automatically apply something to the `Doc`, e.g. to merge spans of tokens.                                                    |
+| [Other functions](/api/pipeline-functions)  | Automatically apply something to the `Doc`, e.g. to merge spans of tokens.                                                    |
 
 ### Other classes {#architecture-other}
 

website/docs/usage/index.md

@@ -8,7 +8,7 @@ menu:
   - ['Changelog', 'changelog']
 ---
 
-spaCy is compatible with **64-bit CPython 2.7+/3.5+** and runs on
+spaCy is compatible with **64-bit CPython 2.7 / 3.5+** and runs on
 **Unix/Linux**, **macOS/OS X** and **Windows**. The latest spaCy releases are
 available over [pip](https://pypi.python.org/pypi/spacy) and
 [conda](https://anaconda.org/conda-forge/spacy).

website/docs/usage/linguistic-features.md

@@ -1149,9 +1149,14 @@ but it also means you'll need a **statistical model** and accurate predictions.
 If your texts are closer to general-purpose news or web text, this should work
 well out-of-the-box. For social media or conversational text that doesn't follow
 the same rules, your application may benefit from a custom rule-based
-implementation. You can either plug a rule-based component into your
+implementation. You can either use the built-in
-[processing pipeline](/usage/processing-pipelines) or use the
+[`Sentencizer`](/api/sentencizer) or plug an entirely custom rule-based function
-`SentenceSegmenter` component with a custom strategy.
+into your [processing pipeline](/usage/processing-pipelines).
+
+spaCy's dependency parser respects already set boundaries, so you can preprocess
+your `Doc` using custom rules _before_ it's parsed. Depending on your text, this
+may also improve accuracy, since the parser is constrained to predict parses
+consistent with the sentence boundaries.
 
 ### Default: Using the dependency parse {#sbd-parser model="parser"}
 
@@ -1168,13 +1173,35 @@ for sent in doc.sents:
     print(sent.text)
 ```
 
-### Setting boundaries manually {#sbd-manual}
+### Rule-based pipeline component {#sbd-component}
 
-spaCy's dependency parser respects already set boundaries, so you can preprocess
+The [`Sentencizer`](/api/sentencizer) component is a
-your `Doc` using custom rules _before_ it's parsed. This can be done by adding a
+[pipeline component](/usage/processing-pipelines) that splits sentences on
-[custom pipeline component](/usage/processing-pipelines). Depending on your
+punctuation like `.`, `!` or `?`. You can plug it into your pipeline if you only
-text, this may also improve accuracy, since the parser is constrained to predict
+need sentence boundaries without the dependency parse.
-parses consistent with the sentence boundaries.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.lang.en import English
+
+nlp = English()  # just the language with no model
+sentencizer = nlp.create_pipe("sentencizer")
+nlp.add_pipe(sentencizer)
+doc = nlp(u"This is a sentence. This is another sentence.")
+for sent in doc.sents:
+    print(sent.text)
+```
+
+### Custom rule-based strategy {id="sbd-custom"}
+
+If you want to implement your own strategy that differs from the default
+rule-based approach of splitting on sentences, you can also create a
+[custom pipeline component](/usage/processing-pipelines#custom-components) that
+takes a `Doc` object and sets the `Token.is_sent_start` attribute on each
+individual token. If set to `False`, the token is explicitly marked as _not_ the
+start of a sentence. If set to `None` (default), it's treated as a missing value
+and can still be overwritten by the parser.
 
 <Infobox title="Important note" variant="warning">
 
@@ -1187,9 +1214,11 @@ adding it to the pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
 
 Here's an example of a component that implements a pre-processing rule for
 splitting on `'...'` tokens. The component is added before the parser, which is
-then used to further segment the text. This approach can be useful if you want
+then used to further segment the text. That's possible, because `is_sent_start`
-to implement **additional** rules specific to your data, while still being able
+is only set to `True` for some of the tokens – all others still specify `None`
-to take advantage of dependency-based sentence segmentation.
+for unset sentence boundaries. This approach can be useful if you want to
+implement **additional** rules specific to your data, while still being able to
+take advantage of dependency-based sentence segmentation.
 
 ```python
 ### {executable="true"}
@@ -1212,62 +1241,6 @@ doc = nlp(text)
 print("After:", [sent.text for sent in doc.sents])
 ```
 
-### Rule-based pipeline component {#sbd-component}
-
-The `sentencizer` component is a
-[pipeline component](/usage/processing-pipelines) that splits sentences on
-punctuation like `.`, `!` or `?`. You can plug it into your pipeline if you only
-need sentence boundaries without the dependency parse. Note that `Doc.sents`
-will **raise an error** if no sentence boundaries are set.
-
-```python
-### {executable="true"}
-import spacy
-from spacy.lang.en import English
-
-nlp = English()  # just the language with no model
-sentencizer = nlp.create_pipe("sentencizer")
-nlp.add_pipe(sentencizer)
-doc = nlp(u"This is a sentence. This is another sentence.")
-for sent in doc.sents:
-    print(sent.text)
-```
-
-### Custom rule-based strategy {#sbd-custom}
-
-If you want to implement your own strategy that differs from the default
-rule-based approach of splitting on sentences, you can also instantiate the
-`SentenceSegmenter` directly and pass in your own strategy. The strategy should
-be a function that takes a `Doc` object and yields a `Span` for each sentence.
-Here's an example of a custom segmentation strategy for splitting on newlines
-only:
-
-```python
-### {executable="true"}
-from spacy.lang.en import English
-from spacy.pipeline import SentenceSegmenter
-
-def split_on_newlines(doc):
-    start = 0
-    seen_newline = False
-    for word in doc:
-        if seen_newline and not word.is_space:
-            yield doc[start:word.i]
-            start = word.i
-            seen_newline = False
-        elif word.text == '\\n':
-            seen_newline = True
-    if start < len(doc):
-        yield doc[start:len(doc)]
-
-nlp = English()  # Just the language with no model
-sentencizer = SentenceSegmenter(nlp.vocab, strategy=split_on_newlines)
-nlp.add_pipe(sentencizer)
-doc = nlp(u"This is a sentence\\n\\nThis is another sentence\\nAnd more")
-for sent in doc.sents:
-    print([token.text for token in sent])
-```
-
 ## Rule-based matching {#rule-based-matching hidden="true"}
 
 <div id="rule-based-matching">

website/docs/usage/processing-pipelines.md

@@ -138,7 +138,7 @@ require them in the pipeline settings in your model's `meta.json`.
 | `ner`               | [`EntityRecognizer`](/api/entityrecognizer)                      | Assign named entities.                                                                        |
 | `textcat`           | [`TextCategorizer`](/api/textcategorizer)                        | Assign text categories.                                                                       |
 | `entity_ruler`      | [`EntityRuler`](/api/entityruler)                                | Assign named entities based on pattern rules.                                                 |
-| `sentencizer`       | [`SentenceSegmenter`](/api/sentencesegmenter)                    | Add rule-based sentence segmentation without the dependency parse.                            |
+| `sentencizer`       | [`Sentencizer`](/api/sentencizer)                                | Add rule-based sentence segmentation without the dependency parse.                            |
 | `merge_noun_chunks` | [`merge_noun_chunks`](/api/pipeline-functions#merge_noun_chunks) | Merge all noun chunks into a single token. Should be added after the tagger and parser.       |
 | `merge_entities`    | [`merge_entities`](/api/pipeline-functions#merge_entities)       | Merge all entities into a single token. Should be added after the entity recognizer.          |
 | `merge_subtokens`   | [`merge_subtokens`](/api/pipeline-functions#merge_subtokens)     | Merge subtokens predicted by the parser into single tokens. Should be added after the parser. |

website/docs/usage/rule-based-matching.md

@@ -404,7 +404,7 @@ class BadHTMLMerger(object):
         for match_id, start, end in matches:
             spans.append(doc[start:end])
         with doc.retokenize() as retokenizer:
-            for span in spans:
+            for span in hashtags:
                 retokenizer.merge(span)
                 for token in span:
                     token._.bad_html = True  # Mark token as bad HTML

website/docs/usage/spacy-101.md

@@ -95,6 +95,21 @@ systems, or to pre-process text for **deep learning**.
   publishing spaCy and other software is called
   [Explosion AI](https://explosion.ai).
 
+<Infobox title="Download the spaCy Cheat Sheet!">
+
+[![spaCy Cheatsheet](../images/cheatsheet.jpg)](http://datacamp-community-prod.s3.amazonaws.com/29aa28bf-570a-4965-8f54-d6a541ae4e06)
+
+For the launch of our
+["Advanced NLP with spaCy"](https://www.datacamp.com/courses/advanced-nlp-with-spacy)
+course on DataCamp we created the first official spaCy cheat sheet! A handy
+two-page reference to the most important concepts and features, from loading
+models and accessing linguistic annotations, to custom pipeline components and
+rule-based matching.
+
+<p><Button to="http://datacamp-community-prod.s3.amazonaws.com/29aa28bf-570a-4965-8f54-d6a541ae4e06" variant="primary">Download</Button></p>
+
+</Infobox>
+
 ## Features {#features}
 
 In the documentation, you'll come across mentions of spaCy's features and

website/docs/usage/v2-1.md

@@ -13,6 +13,8 @@ design changes introduced in [v2.0](/usage/v2). As well as smaller models,
 faster runtime, and many bug fixes, v2.1 also introduces experimental support
 for some exciting new NLP innovations. For the full changelog, see the
 [release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.1.0).
+For more details and a behind-the-scenes look at the new release,
+[see our blog post](https://explosion.ai/blog/spacy-v2-1).
 
 ### BERT/ULMFit/Elmo-style pre-training {#pretraining tag="experimental"}
 
@@ -195,7 +197,7 @@ the existing pages and added some new content:
 - **Universe:** [Videos](/universe/category/videos) and
   [Podcasts](/universe/category/podcasts)
 - **API:** [`EntityRuler`](/api/entityruler)
-- **API:** [`SentenceSegmenter`](/api/sentencesegmenter)
+- **API:** [`Sentencizer`](/api/sentencizer)
 - **API:** [Pipeline functions](/api/pipeline-functions)
 
 ## Backwards incompatibilities {#incompat}
@@ -212,9 +214,8 @@ if all of your models are up to date, you can run the
 
 - Due to difficulties linking our new
   [`blis`](https://github.com/explosion/cython-blis) for faster
-  platform-independent matrix multiplication, this nightly release currently
+  platform-independent matrix multiplication, this release currently **doesn't
-  **doesn't work on Python 2.7 on Windows**. We expect this to be corrected in
+  work on Python 2.7 on Windows**. We expect this to be corrected in the future.
-  the future.
 
 - While the [`Matcher`](/api/matcher) API is fully backwards compatible, its
   algorithm has changed to fix a number of bugs and performance issues. This
@@ -250,9 +251,14 @@ if all of your models are up to date, you can run the
   + data = nlp.tokenizer.to_bytes(exclude=["vocab"])
   ```
 
+- The .pos value for several common English words has changed, due to
+  corrections to long-standing mistakes in the English tag map (see
+  [issue #593](https://github.com/explosion/spaCy/issues/593) and
+  [issue #3311](https://github.com/explosion/spaCy/issues/3311) for details).
+
 - For better compatibility with the Universal Dependencies data, the lemmatizer
   now preserves capitalization, e.g. for proper nouns. See
-  [this issue](https://github.com/explosion/spaCy/issues/3256) for details.
+  [issue #3256](https://github.com/explosion/spaCy/issues/3256) for details.
 
 - The built-in rule-based sentence boundary detector is now only called
   `"sentencizer"` – the name `"sbd"` is deprecated.

website/meta/languages.json

@@ -117,6 +117,7 @@
         { "code": "sk", "name": "Slovak" },
         { "code": "sl", "name": "Slovenian" },
         { "code": "sq", "name": "Albanian" },
+        { "code": "et", "name": "Estonian" },
         {
             "code": "th",
             "name": "Thai",

website/meta/sidebars.json

@@ -79,7 +79,7 @@
                     { "text": "Matcher", "url": "/api/matcher" },
                     { "text": "PhraseMatcher", "url": "/api/phrasematcher" },
                     { "text": "EntityRuler", "url": "/api/entityruler" },
-                    { "text": "SentenceSegmenter", "url": "/api/sentencesegmenter" },
+                    { "text": "Sentencizer", "url": "/api/sentencizer" },
                     { "text": "Other Functions", "url": "/api/pipeline-functions" }
                 ]
             },

website/meta/site.json

@@ -28,8 +28,8 @@
     },
     "spacyVersion": "2.1",
     "binderUrl": "ines/spacy-io-binder",
-    "binderBranch": "nightly",
+    "binderBranch": "live",
-    "binderVersion": "2.1.0a9",
+    "binderVersion": "2.1.3",
     "sections": [
         { "id": "usage", "title": "Usage Documentation", "theme": "blue" },
         { "id": "models", "title": "Models Documentation", "theme": "blue" },

website/src/widgets/landing.js

@@ -31,24 +31,21 @@ import spacy
 nlp = spacy.load("en_core_web_sm")
 
 # Process whole documents
-text = (u"When Sebastian Thrun started working on self-driving cars at "
+text = ("When Sebastian Thrun started working on self-driving cars at "
-        u"Google in 2007, few people outside of the company took him "
+        "Google in 2007, few people outside of the company took him "
-        u"seriously. “I can tell you very senior CEOs of major American "
+        "seriously. “I can tell you very senior CEOs of major American "
-        u"car companies would shake my hand and turn away because I wasn’t "
+        "car companies would shake my hand and turn away because I wasn’t "
-        u"worth talking to,” said Thrun, now the co-founder and CEO of "
+        "worth talking to,” said Thrun, in an interview with Recode earlier "
-        u"online higher education startup Udacity, in an interview with "
+        "this week.")
-        u"Recode earlier this week.")
 doc = nlp(text)
 
+# Analyze syntax
+print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
+print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])
+
 # Find named entities, phrases and concepts
 for entity in doc.ents:
     print(entity.text, entity.label_)
-
-# Determine semantic similarities
-doc1 = nlp(u"my fries were super gross")
-doc2 = nlp(u"such disgusting fries")
-similarity = doc1.similarity(doc2)
-print(doc1.text, doc2.text, similarity)
 `
 
 /**
@@ -218,7 +215,7 @@ const Landing = ({ data }) => {
                     <H2>Benchmarks</H2>
                     <p>
                         In 2015, independent researchers from Emory University and Yahoo! Labs
-                        showed that spaCy offered the
+                        showed that spaCy offered the{' '}
                         <strong>fastest syntactic parser in the world</strong> and that its accuracy
                         was <strong>within 1% of the best</strong> available (
                         <Link to="https://aclweb.org/anthology/P/P15/P15-1038.pdf">