Merge remote-tracking branch 'upstream/master' into chore/update-develop-from-master-v3.5-1

.github/azure-steps.yml

@@ -54,12 +54,12 @@ steps:
     condition: eq(${{ parameters.gpu }}, true)
 
   - script: |
-      ${{ parameters.prefix }} python -m pytest --pyargs spacy
+      ${{ parameters.prefix }} python -m pytest --pyargs spacy -W error
     displayName: "Run CPU tests"
     condition: eq(${{ parameters.gpu }}, false)
 
   - script: |
-      ${{ parameters.prefix }} python -m pytest --pyargs spacy -p spacy.tests.enable_gpu
+      ${{ parameters.prefix }} python -m pytest --pyargs spacy -W error -p spacy.tests.enable_gpu
     displayName: "Run GPU tests"
     condition: eq(${{ parameters.gpu }}, true)
 

.github/no-response.yml

@@ -1,13 +0,0 @@
-# Configuration for probot-no-response - https://github.com/probot/no-response
-
-# Number of days of inactivity before an Issue is closed for lack of response
-daysUntilClose: 14
-# Label requiring a response
-responseRequiredLabel: more-info-needed
-# Comment to post when closing an Issue for lack of response. Set to `false` to disable
-closeComment: >
-  This issue has been automatically closed because there has been no response
-  to a request for more information from the original author. With only the
-  information that is currently in the issue, there's not enough information
-  to take action. If you're the original author, feel free to reopen the issue
-  if you have or find the answers needed to investigate further.

.github/workflows/issue-manager.yml

@@ -15,7 +15,7 @@ jobs:
   issue-manager:
     runs-on: ubuntu-latest
     steps:
-      - uses: tiangolo/issue-manager@0.2.1
+      - uses: tiangolo/issue-manager@0.4.0
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
           config: >
@@ -25,5 +25,11 @@ jobs:
                 "message": "This issue has been automatically closed because it was answered and there was no follow-up discussion.",
                 "remove_label_on_comment": true,
                 "remove_label_on_close": true
+              },
+              "more-info-needed": {
+                "delay": "P7D",
+                "message": "This issue has been automatically closed because there has been no response to a request for more information from the original author. With only the information that is currently in the issue, there's not enough information to take action. If you're the original author, feel free to reopen the issue if you have or find the answers needed to investigate further.",
+                "remove_label_on_comment": true,
+                "remove_label_on_close": true
               }
             }

azure-pipelines.yml

@@ -32,7 +32,7 @@ jobs:
           versionSpec: "3.7"
       - script: |
           pip install flake8==3.9.2
-          python -m flake8 spacy --count --select=E901,E999,F821,F822,F823 --show-source --statistics
+          python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
         displayName: "flake8"
 
   - job: "Test"

extra/DEVELOPER_DOCS/Code Conventions.md

@@ -191,6 +191,8 @@ def load_model(name: str) -> "Language":
     ...
 ```
 
+Note that we typically put the `from typing` import statements on the first line(s) of the Python module.
+
 ## Structuring logic
 
 ### Positional and keyword arguments
@@ -275,6 +277,27 @@ If you have to use `try`/`except`, make sure to only include what's **absolutely
 +     return [v.strip() for v in value.split(",")]
 ```
 
+### Numeric comparisons
+
+For numeric comparisons, as a general rule we always use `<` and `>=` and avoid the usage of `<=` and `>`. This is to ensure we consistently
+apply inclusive lower bounds and exclusive upper bounds, helping to prevent off-by-one errors.
+
+One exception to this rule is the ternary case. With a chain like
+
+```python
+if value >= 0 and value < max:
+    ...
+```
+
+it's fine to rewrite this to the shorter form
+
+```python
+if 0 <= value < max:
+    ...
+```
+
+even though this requires the usage of the `<=` operator.
+
 ### Iteration and comprehensions
 
 We generally avoid using built-in functions like `filter` or `map` in favor of list or generator comprehensions.

spacy/displacy/__init__.py

@@ -123,7 +123,8 @@ def app(environ, start_response):
 def parse_deps(orig_doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
     """Generate dependency parse in {'words': [], 'arcs': []} format.
 
-    doc (Doc): Document do parse.
+    orig_doc (Doc): Document to parse.
+    options (Dict[str, Any]): Dependency parse specific visualisation options.
     RETURNS (dict): Generated dependency parse keyed by words and arcs.
     """
     doc = Doc(orig_doc.vocab).from_bytes(
@@ -209,7 +210,7 @@ def parse_ents(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
 
 
 def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
-    """Generate spans in [{start: i, end: i, label: 'label'}] format.
+    """Generate spans in [{start_token: i, end_token: i, label: 'label'}] format.
 
     doc (Doc): Document to parse.
     options (Dict[str, any]): Span-specific visualisation options.

spacy/errors.py

@@ -16,8 +16,8 @@ def setup_default_warnings():
     filter_warning("ignore", error_msg="numpy.dtype size changed")  # noqa
     filter_warning("ignore", error_msg="numpy.ufunc size changed")  # noqa
 
-    # warn about entity_ruler & matcher having no patterns only once
+    # warn about entity_ruler, span_ruler & matcher having no patterns only once
-    for pipe in ["matcher", "entity_ruler"]:
+    for pipe in ["matcher", "entity_ruler", "span_ruler"]:
         filter_warning("once", error_msg=Warnings.W036.format(name=pipe))
 
     # warn once about lemmatizer without required POS
@@ -389,7 +389,7 @@ class Errors(metaclass=ErrorsWithCodes):
             "consider using doc.spans instead.")
     E106 = ("Can't find `doc._.{attr}` attribute specified in the underscore "
             "settings: {opts}")
-    E107 = ("Value of `doc._.{attr}` is not JSON-serializable: {value}")
+    E107 = ("Value of custom attribute `{attr}` is not JSON-serializable: {value}")
     E109 = ("Component '{name}' could not be run. Did you forget to "
             "call `initialize()`?")
     E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
@@ -535,11 +535,12 @@ class Errors(metaclass=ErrorsWithCodes):
     E198 = ("Unable to return {n} most similar vectors for the current vectors "
             "table, which contains {n_rows} vectors.")
     E199 = ("Unable to merge 0-length span at `doc[{start}:{end}]`.")
-    E200 = ("Can't yet set {attr} from Span. Vote for this feature on the "
+    E200 = ("Can't set {attr} from Span.")
-            "issue tracker: http://github.com/explosion/spaCy/issues")
     E202 = ("Unsupported {name} mode '{mode}'. Supported modes: {modes}.")
 
     # New errors added in v3.x
+    E853 = ("Unsupported component factory name '{name}'. The character '.' is "
+            "not permitted in factory names.")
     E854 = ("Unable to set doc.ents. Check that the 'ents_filter' does not "
             "permit overlapping spans.")
     E855 = ("Invalid {obj}: {obj} is not from the same doc.")

spacy/lang/ko/punctuation.py

@@ -3,7 +3,7 @@ from ..punctuation import TOKENIZER_INFIXES as BASE_TOKENIZER_INFIXES
 
 
 _infixes = (
-    ["·", "ㆍ", "\(", "\)"]
+    ["·", "ㆍ", r"\(", r"\)"]
     + [r"(?<=[0-9])~(?=[0-9-])"]
     + LIST_QUOTES
     + BASE_TOKENIZER_INFIXES

spacy/lang/lg/__init__.py

@@ -0,0 +1,18 @@
+from .stop_words import STOP_WORDS
+from .lex_attrs import LEX_ATTRS
+from .punctuation import TOKENIZER_INFIXES
+from ...language import Language, BaseDefaults
+
+
+class LugandaDefaults(BaseDefaults):
+    lex_attr_getters = LEX_ATTRS
+    infixes = TOKENIZER_INFIXES
+    stop_words = STOP_WORDS
+
+
+class Luganda(Language):
+    lang = "lg"
+    Defaults = LugandaDefaults
+
+
+__all__ = ["Luganda"]

spacy/lang/lg/examples.py

@@ -0,0 +1,17 @@
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.lg.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+sentences = [
+    "Mpa ebyafaayo ku byalo Nakatu ne Nkajja",
+    "Okuyita Ttembo kitegeeza kugwa ddalu",
+    "Ekifumu kino kyali kya mulimu ki?",
+    "Ekkovu we liyise wayitibwa mukululo",
+    "Akola mulimu ki oguvaamu ssente?",
+    "Emisumaali egikomerera embaawo giyitibwa nninga",
+    "Abooluganda ab’emmamba ababiri",
+    "Ekisaawe ky'ebyenjigiriza kya mugaso nnyo",
+]

spacy/lang/lg/lex_attrs.py

@@ -0,0 +1,95 @@
+from ...attrs import LIKE_NUM
+
+_num_words = [
+    "nnooti",  # Zero
+    "zeero",  # zero
+    "emu",  # one
+    "bbiri",  # two
+    "ssatu",  # three
+    "nnya",  # four
+    "ttaano",  # five
+    "mukaaga",  # six
+    "musanvu",  # seven
+    "munaana",  # eight
+    "mwenda",  # nine
+    "kkumi",  # ten
+    "kkumi n'emu",  # eleven
+    "kkumi na bbiri",  # twelve
+    "kkumi na ssatu",  # thirteen
+    "kkumi na nnya",  # forteen
+    "kkumi na ttaano",  # fifteen
+    "kkumi na mukaaga",  # sixteen
+    "kkumi na musanvu",  # seventeen
+    "kkumi na munaana",  # eighteen
+    "kkumi na mwenda",  # nineteen
+    "amakumi abiri",  # twenty
+    "amakumi asatu",  # thirty
+    "amakumi ana",  # forty
+    "amakumi ataano",  # fifty
+    "nkaaga",  # sixty
+    "nsanvu",  # seventy
+    "kinaana",  # eighty
+    "kyenda",  # ninety
+    "kikumi",  # hundred
+    "lukumi",  # thousand
+    "kakadde",  # million
+    "kawumbi",  # billion
+    "kase",  # trillion
+    "katabalika",  # quadrillion
+    "keesedde",  # gajillion
+    "kafukunya",  # bazillion
+    "ekisooka",  # first
+    "ekyokubiri",  # second
+    "ekyokusatu",  # third
+    "ekyokuna",  # fourth
+    "ekyokutaano",  # fifith
+    "ekyomukaaga",  # sixth
+    "ekyomusanvu",  # seventh
+    "eky'omunaana",  # eighth
+    "ekyomwenda",  # nineth
+    "ekyekkumi",  # tenth
+    "ekyekkumi n'ekimu",  # eleventh
+    "ekyekkumi n'ebibiri",  # twelveth
+    "ekyekkumi n'ebisatu",  # thirteenth
+    "ekyekkumi n'ebina",  # fourteenth
+    "ekyekkumi n'ebitaano",  # fifteenth
+    "ekyekkumi n'omukaaga",  # sixteenth
+    "ekyekkumi n'omusanvu",  # seventeenth
+    "ekyekkumi n'omunaana",  # eigteenth
+    "ekyekkumi n'omwenda",  # nineteenth
+    "ekyamakumi abiri",  # twentieth
+    "ekyamakumi asatu",  # thirtieth
+    "ekyamakumi ana",  # fortieth
+    "ekyamakumi ataano",  # fiftieth
+    "ekyenkaaga",  # sixtieth
+    "ekyensanvu",  # seventieth
+    "ekyekinaana",  # eightieth
+    "ekyekyenda",  # ninetieth
+    "ekyekikumi",  # hundredth
+    "ekyolukumi",  # thousandth
+    "ekyakakadde",  # millionth
+    "ekyakawumbi",  # billionth
+    "ekyakase",  # trillionth
+    "ekyakatabalika",  # quadrillionth
+    "ekyakeesedde",  # gajillionth
+    "ekyakafukunya",  # bazillionth
+]
+
+
+def like_num(text):
+    if text.startswith(("+", "-", "±", "~")):
+        text = text[1:]
+    text = text.replace(",", "").replace(".", "")
+    if text.isdigit():
+        return True
+    if text.count("/") == 1:
+        num, denom = text.split("/")
+        if num.isdigit() and denom.isdigit():
+            return True
+    text_lower = text.lower()
+    if text_lower in _num_words:
+        return True
+    return False
+
+
+LEX_ATTRS = {LIKE_NUM: like_num}

spacy/lang/lg/punctuation.py

@@ -0,0 +1,19 @@
+from ..char_classes import LIST_ELLIPSES, LIST_ICONS, HYPHENS
+from ..char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA
+
+_infixes = (
+    LIST_ELLIPSES
+    + LIST_ICONS
+    + [
+        r"(?<=[0-9])[+\-\*^](?=[0-9-])",
+        r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
+            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
+        ),
+        r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
+        r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
+    ]
+)
+
+
+TOKENIZER_INFIXES = _infixes

spacy/lang/lg/stop_words.py

@@ -0,0 +1,19 @@
+STOP_WORDS = set(
+    """
+abadde abalala abamu abangi abava ajja ali alina ani anti ateekeddwa atewamu
+atya awamu aweebwa ayinza ba baali babadde babalina bajja
+bajjanewankubade bali balina bandi bangi bano bateekeddwa baweebwa bayina bebombi beera bibye
+bimu bingi bino bo bokka bonna buli bulijjo bulungi bwabwe bwaffe bwayo bwe bwonna bya byabwe
+byaffe byebimu byonna ddaa ddala ddi e ebimu ebiri ebweruobulungi ebyo edda ejja ekirala ekyo
+endala engeri ennyo era erimu erina ffe ffenna ga gujja gumu gunno guno gwa gwe kaseera kati
+kennyini ki kiki kikino kikye kikyo kino kirungi kki ku kubangabyombi kubangaolwokuba kudda
+kuva kuwa kwegamba kyaffe kye kyekimuoyo kyekyo kyonna leero liryo lwa lwaki lyabwezaabwe
+lyaffe lyange mbadde mingi mpozzi mu mulinaoyina munda mwegyabwe nolwekyo nabadde nabo nandiyagadde
+nandiye nanti naye ne nedda neera nga nnyingi nnyini nnyinza nnyo nti nyinza nze oba ojja okudda
+okugenda okuggyako okutuusa okuva okuwa oli olina oluvannyuma olwekyobuva omuli ono osobola otya
+oyina oyo seetaaga si sinakindi singa talina tayina tebaali tebaalina tebayina terina tetulina
+tetuteekeddwa tewali teyalina teyayina tolina tu tuyina tulina tuyina twafuna twetaaga wa wabula
+wabweru wadde waggulunnina wakati waliwobangi waliyo wandi wange wano wansi weebwa yabadde yaffe
+ye yenna yennyini yina yonna ziba zijja zonna
+""".split()
+)

spacy/lang/nl/syntax_iterators.py

@@ -40,6 +40,7 @@ def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
     span_label = doc.vocab.strings.add("NP")
 
     # Only NOUNS and PRONOUNS matter
+    end_span = -1
     for i, word in enumerate(filter(lambda x: x.pos in [PRON, NOUN], doclike)):
         # For NOUNS
         # Pick children from syntactic parse (only those with certain dependencies)
@@ -58,15 +59,17 @@ def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
             children_i = [c.i for c in children] + [word.i]
 
             start_span = min(children_i)
-            end_span = max(children_i) + 1
+            if start_span >= end_span:
-            yield start_span, end_span, span_label
+                end_span = max(children_i) + 1
+                yield start_span, end_span, span_label
 
         # PRONOUNS only if it is the subject of a verb
         elif word.pos == PRON:
             if word.dep in pronoun_deps:
                 start_span = word.i
-                end_span = word.i + 1
+                if start_span >= end_span:
-                yield start_span, end_span, span_label
+                    end_span = word.i + 1
+                    yield start_span, end_span, span_label
 
 
 SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

spacy/language.py

@@ -465,6 +465,8 @@ class Language:
         """
         if not isinstance(name, str):
             raise ValueError(Errors.E963.format(decorator="factory"))
+        if "." in name:
+            raise ValueError(Errors.E853.format(name=name))
         if not isinstance(default_config, dict):
             err = Errors.E962.format(
                 style="default config", name=name, cfg_type=type(default_config)
@@ -543,8 +545,11 @@ class Language:
 
         DOCS: https://spacy.io/api/language#component
         """
-        if name is not None and not isinstance(name, str):
+        if name is not None:
-            raise ValueError(Errors.E963.format(decorator="component"))
+            if not isinstance(name, str):
+                raise ValueError(Errors.E963.format(decorator="component"))
+            if "." in name:
+                raise ValueError(Errors.E853.format(name=name))
         component_name = name if name is not None else util.get_object_name(func)
 
         def add_component(component_func: "Pipe") -> Callable:

spacy/schemas.py

@@ -207,7 +207,7 @@ class TokenPatternOperatorSimple(str, Enum):
 
 
 class TokenPatternOperatorMinMax(ConstrainedStr):
-    regex = re.compile("^({\d+}|{\d+,\d*}|{\d*,\d+})$")
+    regex = re.compile(r"^({\d+}|{\d+,\d*}|{\d*,\d+})$")
 
 
 TokenPatternOperator = Union[TokenPatternOperatorSimple, TokenPatternOperatorMinMax]
@@ -514,6 +514,14 @@ class DocJSONSchema(BaseModel):
     tokens: List[Dict[StrictStr, Union[StrictStr, StrictInt]]] = Field(
         ..., title="Token information - ID, start, annotations"
     )
-    _: Optional[Dict[StrictStr, Any]] = Field(
+    underscore_doc: Optional[Dict[StrictStr, Any]] = Field(
-        None, title="Any custom data stored in the document's _ attribute"
+        None,
+        title="Any custom data stored in the document's _ attribute",
+        alias="_",
+    )
+    underscore_token: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
+        None, title="Any custom data stored in the token's _ attribute"
+    )
+    underscore_span: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
+        None, title="Any custom data stored in the span's _ attribute"
     )

spacy/tests/conftest.py

@@ -261,6 +261,11 @@ def lb_tokenizer():
     return get_lang_class("lb")().tokenizer
 
 
+@pytest.fixture(scope="session")
+def lg_tokenizer():
+    return get_lang_class("lg")().tokenizer
+
+
 @pytest.fixture(scope="session")
 def lt_tokenizer():
     return get_lang_class("lt")().tokenizer

spacy/tests/doc/test_doc_api.py

@@ -3,6 +3,7 @@ import weakref
 import numpy
 from numpy.testing import assert_array_equal
 import pytest
+import warnings
 from thinc.api import NumpyOps, get_current_ops
 
 from spacy.attrs import DEP, ENT_IOB, ENT_TYPE, HEAD, IS_ALPHA, MORPH, POS
@@ -529,9 +530,9 @@ def test_doc_from_array_sent_starts(en_vocab):
     # no warning using default attrs
     attrs = doc._get_array_attrs()
     arr = doc.to_array(attrs)
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
         new_doc.from_array(attrs, arr)
-        assert len(record) == 0
     # only SENT_START uses SENT_START
     attrs = [SENT_START]
     arr = doc.to_array(attrs)

spacy/tests/doc/test_json_doc_conversion.py

@@ -1,12 +1,15 @@
 import pytest
 import spacy
 from spacy import schemas
-from spacy.tokens import Doc, Span
+from spacy.tokens import Doc, Span, Token
+import srsly
+from .test_underscore import clean_underscore  # noqa: F401
 
 
 @pytest.fixture()
 def doc(en_vocab):
     words = ["c", "d", "e"]
+    spaces = [True, True, True]
     pos = ["VERB", "NOUN", "NOUN"]
     tags = ["VBP", "NN", "NN"]
     heads = [0, 0, 1]
@@ -17,6 +20,7 @@ def doc(en_vocab):
     return Doc(
         en_vocab,
         words=words,
+        spaces=spaces,
         pos=pos,
         tags=tags,
         heads=heads,
@@ -45,6 +49,47 @@ def doc_without_deps(en_vocab):
     )
 
 
+@pytest.fixture()
+def doc_json():
+    return {
+        "text": "c d e ",
+        "ents": [{"start": 2, "end": 3, "label": "ORG"}],
+        "sents": [{"start": 0, "end": 5}],
+        "tokens": [
+            {
+                "id": 0,
+                "start": 0,
+                "end": 1,
+                "tag": "VBP",
+                "pos": "VERB",
+                "morph": "Feat1=A",
+                "dep": "ROOT",
+                "head": 0,
+            },
+            {
+                "id": 1,
+                "start": 2,
+                "end": 3,
+                "tag": "NN",
+                "pos": "NOUN",
+                "morph": "Feat1=B",
+                "dep": "dobj",
+                "head": 0,
+            },
+            {
+                "id": 2,
+                "start": 4,
+                "end": 5,
+                "tag": "NN",
+                "pos": "NOUN",
+                "morph": "Feat1=A|Feat2=D",
+                "dep": "dobj",
+                "head": 1,
+            },
+        ],
+    }
+
+
 def test_doc_to_json(doc):
     json_doc = doc.to_json()
     assert json_doc["text"] == "c d e "
@@ -56,7 +101,8 @@ def test_doc_to_json(doc):
     assert json_doc["ents"][0]["start"] == 2  # character offset!
     assert json_doc["ents"][0]["end"] == 3  # character offset!
     assert json_doc["ents"][0]["label"] == "ORG"
-    assert not schemas.validate(schemas.DocJSONSchema, json_doc)
+    assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
+    assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
 
 
 def test_doc_to_json_underscore(doc):
@@ -64,11 +110,96 @@ def test_doc_to_json_underscore(doc):
     Doc.set_extension("json_test2", default=False)
     doc._.json_test1 = "hello world"
     doc._.json_test2 = [1, 2, 3]
+
     json_doc = doc.to_json(underscore=["json_test1", "json_test2"])
     assert "_" in json_doc
     assert json_doc["_"]["json_test1"] == "hello world"
     assert json_doc["_"]["json_test2"] == [1, 2, 3]
-    assert not schemas.validate(schemas.DocJSONSchema, json_doc)
+    assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
+    assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
+
+
+def test_doc_to_json_with_token_span_attributes(doc):
+    Doc.set_extension("json_test1", default=False)
+    Doc.set_extension("json_test2", default=False)
+    Token.set_extension("token_test", default=False)
+    Span.set_extension("span_test", default=False)
+
+    doc._.json_test1 = "hello world"
+    doc._.json_test2 = [1, 2, 3]
+    doc[0:1]._.span_test = "span_attribute"
+    doc[0]._.token_test = 117
+    doc.spans["span_group"] = [doc[0:1]]
+    json_doc = doc.to_json(
+        underscore=["json_test1", "json_test2", "token_test", "span_test"]
+    )
+
+    assert "_" in json_doc
+    assert json_doc["_"]["json_test1"] == "hello world"
+    assert json_doc["_"]["json_test2"] == [1, 2, 3]
+    assert "underscore_token" in json_doc
+    assert "underscore_span" in json_doc
+    assert json_doc["underscore_token"]["token_test"]["value"] == 117
+    assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
+    assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
+    assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
+
+
+def test_doc_to_json_with_custom_user_data(doc):
+    Doc.set_extension("json_test", default=False)
+    Token.set_extension("token_test", default=False)
+    Span.set_extension("span_test", default=False)
+
+    doc._.json_test = "hello world"
+    doc[0:1]._.span_test = "span_attribute"
+    doc[0]._.token_test = 117
+    json_doc = doc.to_json(underscore=["json_test", "token_test", "span_test"])
+    doc.user_data["user_data_test"] = 10
+    doc.user_data[("user_data_test2", True)] = 10
+
+    assert "_" in json_doc
+    assert json_doc["_"]["json_test"] == "hello world"
+    assert "underscore_token" in json_doc
+    assert "underscore_span" in json_doc
+    assert json_doc["underscore_token"]["token_test"]["value"] == 117
+    assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
+    assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
+    assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
+
+
+def test_doc_to_json_with_token_span_same_identifier(doc):
+    Doc.set_extension("my_ext", default=False)
+    Token.set_extension("my_ext", default=False)
+    Span.set_extension("my_ext", default=False)
+
+    doc._.my_ext = "hello world"
+    doc[0:1]._.my_ext = "span_attribute"
+    doc[0]._.my_ext = 117
+    json_doc = doc.to_json(underscore=["my_ext"])
+
+    assert "_" in json_doc
+    assert json_doc["_"]["my_ext"] == "hello world"
+    assert "underscore_token" in json_doc
+    assert "underscore_span" in json_doc
+    assert json_doc["underscore_token"]["my_ext"]["value"] == 117
+    assert json_doc["underscore_span"]["my_ext"]["value"] == "span_attribute"
+    assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
+    assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
+
+
+def test_doc_to_json_with_token_attributes_missing(doc):
+    Token.set_extension("token_test", default=False)
+    Span.set_extension("span_test", default=False)
+
+    doc[0:1]._.span_test = "span_attribute"
+    doc[0]._.token_test = 117
+    json_doc = doc.to_json(underscore=["span_test"])
+
+    assert "underscore_token" in json_doc
+    assert "underscore_span" in json_doc
+    assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
+    assert "token_test" not in json_doc["underscore_token"]
+    assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
 
 
 def test_doc_to_json_underscore_error_attr(doc):
@@ -94,11 +225,29 @@ def test_doc_to_json_span(doc):
     assert len(json_doc["spans"]) == 1
     assert len(json_doc["spans"]["test"]) == 2
     assert json_doc["spans"]["test"][0]["start"] == 0
-    assert not schemas.validate(schemas.DocJSONSchema, json_doc)
+    assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
 
 
 def test_json_to_doc(doc):
-    new_doc = Doc(doc.vocab).from_json(doc.to_json(), validate=True)
+    json_doc = doc.to_json()
+    json_doc = srsly.json_loads(srsly.json_dumps(json_doc))
+    new_doc = Doc(doc.vocab).from_json(json_doc, validate=True)
+    assert new_doc.text == doc.text == "c d e "
+    assert len(new_doc) == len(doc) == 3
+    assert new_doc[0].pos == doc[0].pos
+    assert new_doc[0].tag == doc[0].tag
+    assert new_doc[0].dep == doc[0].dep
+    assert new_doc[0].head.idx == doc[0].head.idx
+    assert new_doc[0].lemma == doc[0].lemma
+    assert len(new_doc.ents) == 1
+    assert new_doc.ents[0].start == 1
+    assert new_doc.ents[0].end == 2
+    assert new_doc.ents[0].label_ == "ORG"
+    assert doc.to_bytes() == new_doc.to_bytes()
+
+
+def test_json_to_doc_compat(doc, doc_json):
+    new_doc = Doc(doc.vocab).from_json(doc_json, validate=True)
     new_tokens = [token for token in new_doc]
     assert new_doc.text == doc.text == "c d e "
     assert len(new_tokens) == len([token for token in doc]) == 3
@@ -114,11 +263,8 @@ def test_json_to_doc(doc):
 
 
 def test_json_to_doc_underscore(doc):
-    if not Doc.has_extension("json_test1"):
+    Doc.set_extension("json_test1", default=False)
-        Doc.set_extension("json_test1", default=False)
+    Doc.set_extension("json_test2", default=False)
-    if not Doc.has_extension("json_test2"):
-        Doc.set_extension("json_test2", default=False)
-
     doc._.json_test1 = "hello world"
     doc._.json_test2 = [1, 2, 3]
     json_doc = doc.to_json(underscore=["json_test1", "json_test2"])
@@ -126,6 +272,34 @@ def test_json_to_doc_underscore(doc):
     assert all([new_doc.has_extension(f"json_test{i}") for i in range(1, 3)])
     assert new_doc._.json_test1 == "hello world"
     assert new_doc._.json_test2 == [1, 2, 3]
+    assert doc.to_bytes() == new_doc.to_bytes()
+
+
+def test_json_to_doc_with_token_span_attributes(doc):
+    Doc.set_extension("json_test1", default=False)
+    Doc.set_extension("json_test2", default=False)
+    Token.set_extension("token_test", default=False)
+    Span.set_extension("span_test", default=False)
+    doc._.json_test1 = "hello world"
+    doc._.json_test2 = [1, 2, 3]
+    doc[0:1]._.span_test = "span_attribute"
+    doc[0]._.token_test = 117
+
+    json_doc = doc.to_json(
+        underscore=["json_test1", "json_test2", "token_test", "span_test"]
+    )
+    json_doc = srsly.json_loads(srsly.json_dumps(json_doc))
+    new_doc = Doc(doc.vocab).from_json(json_doc, validate=True)
+
+    assert all([new_doc.has_extension(f"json_test{i}") for i in range(1, 3)])
+    assert new_doc._.json_test1 == "hello world"
+    assert new_doc._.json_test2 == [1, 2, 3]
+    assert new_doc[0]._.token_test == 117
+    assert new_doc[0:1]._.span_test == "span_attribute"
+    assert new_doc.user_data == doc.user_data
+    assert new_doc.to_bytes(exclude=["user_data"]) == doc.to_bytes(
+        exclude=["user_data"]
+    )
 
 
 def test_json_to_doc_spans(doc):

spacy/tests/lang/lg/test_tokenizer.py

@@ -0,0 +1,15 @@
+import pytest
+
+LG_BASIC_TOKENIZATION_TESTS = [
+    (
+        "Abooluganda ab’emmamba ababiri",
+        ["Abooluganda", "ab’emmamba", "ababiri"],
+    ),
+]
+
+
+@pytest.mark.parametrize("text,expected_tokens", LG_BASIC_TOKENIZATION_TESTS)
+def test_lg_tokenizer_basic(lg_tokenizer, text, expected_tokens):
+    tokens = lg_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/lang/nl/test_noun_chunks.py

@@ -1,5 +1,6 @@
-from spacy.tokens import Doc
 import pytest
+from spacy.tokens import Doc
+from spacy.util import filter_spans
 
 
 @pytest.fixture
@@ -207,3 +208,18 @@ def test_chunking(nl_sample, nl_reference_chunking):
     """
     chunks = [s.text.lower() for s in nl_sample.noun_chunks]
     assert chunks == nl_reference_chunking
+
+
+@pytest.mark.issue(10846)
+def test_no_overlapping_chunks(nl_vocab):
+    # fmt: off
+    doc = Doc(
+        nl_vocab,
+        words=["Dit", "programma", "wordt", "beschouwd", "als", "'s", "werelds", "eerste", "computerprogramma"],
+        deps=["det", "nsubj:pass", "aux:pass", "ROOT", "mark", "det", "fixed", "amod", "xcomp"],
+        heads=[1, 3, 3, 3, 8, 8, 5, 8, 3],
+        pos=["DET", "NOUN", "AUX", "VERB", "SCONJ", "DET", "NOUN", "ADJ", "NOUN"],
+    )
+    # fmt: on
+    chunks = list(doc.noun_chunks)
+    assert filter_spans(chunks) == chunks

spacy/tests/lang/ru/test_lemmatizer.py

@@ -2,6 +2,9 @@ import pytest
 from spacy.tokens import Doc
 
 
+pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
+
+
 def test_ru_doc_lemmatization(ru_lemmatizer):
     words = ["мама", "мыла", "раму"]
     pos = ["NOUN", "VERB", "NOUN"]

spacy/tests/lang/uk/test_lemmatizer.py

@@ -1,6 +1,10 @@
+import pytest
 from spacy.tokens import Doc
 
 
+pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
+
+
 def test_uk_lemmatizer(uk_lemmatizer):
     """Check that the default uk lemmatizer runs."""
     doc = Doc(uk_lemmatizer.vocab, words=["a", "b", "c"])

spacy/tests/matcher/test_phrase_matcher.py

@@ -1,4 +1,5 @@
 import pytest
+import warnings
 import srsly
 from mock import Mock
 
@@ -344,13 +345,13 @@ def test_phrase_matcher_validation(en_vocab):
         matcher.add("TEST1", [doc1])
     with pytest.warns(UserWarning):
         matcher.add("TEST2", [doc2])
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
         matcher.add("TEST3", [doc3])
-        assert not record.list
     matcher = PhraseMatcher(en_vocab, attr="POS", validate=True)
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
         matcher.add("TEST4", [doc2])
-        assert not record.list
 
 
 def test_attr_validation(en_vocab):

spacy/tests/pipeline/test_entity_linker.py

@@ -1048,6 +1048,10 @@ def test_no_gold_ents(patterns):
     for eg in train_examples:
         eg.predicted = ruler(eg.predicted)
 
+    # Entity ruler is no longer needed (initialization below wipes out the
+    # patterns and causes warnings)
+    nlp.remove_pipe("entity_ruler")
+
     def create_kb(vocab):
         # create artificial KB
         mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)

spacy/tests/test_language.py

@@ -659,3 +659,14 @@ def test_multiprocessing_gpu_warning(nlp2, texts):
             # Trigger multi-processing.
             for _ in docs:
                 pass
+
+
+def test_dot_in_factory_names(nlp):
+    Language.component("my_evil_component", func=evil_component)
+    nlp.add_pipe("my_evil_component")
+
+    with pytest.raises(ValueError, match="not permitted"):
+        Language.component("my.evil.component.v1", func=evil_component)
+
+    with pytest.raises(ValueError, match="not permitted"):
+        Language.factory("my.evil.component.v1", func=evil_component)

spacy/tests/training/test_new_example.py

@@ -431,3 +431,41 @@ def test_Example_aligned_whitespace(en_vocab):
 
     example = Example(predicted, reference)
     assert example.get_aligned("TAG", as_string=True) == tags
+
+
+@pytest.mark.issue("11260")
+def test_issue11260():
+    annots = {
+        "words": ["I", "like", "New", "York", "."],
+        "spans": {
+            "cities": [(7, 15, "LOC", "")],
+            "people": [(0, 1, "PERSON", "")],
+        },
+    }
+    vocab = Vocab()
+    predicted = Doc(vocab, words=annots["words"])
+    example = Example.from_dict(predicted, annots)
+    assert len(example.reference.spans["cities"]) == 1
+    assert len(example.reference.spans["people"]) == 1
+
+    output_dict = example.to_dict()
+    assert "spans" in output_dict["doc_annotation"]
+    assert output_dict["doc_annotation"]["spans"]["cities"] == annots["spans"]["cities"]
+    assert output_dict["doc_annotation"]["spans"]["people"] == annots["spans"]["people"]
+
+    output_example = Example.from_dict(predicted, output_dict)
+
+    assert len(output_example.reference.spans["cities"]) == len(
+        example.reference.spans["cities"]
+    )
+    assert len(output_example.reference.spans["people"]) == len(
+        example.reference.spans["people"]
+    )
+    for span in example.reference.spans["cities"]:
+        assert span.label_ == "LOC"
+        assert span.text == "New York"
+        assert span.start_char == 7
+    for span in example.reference.spans["people"]:
+        assert span.label_ == "PERSON"
+        assert span.text == "I"
+        assert span.start_char == 0

spacy/tokens/doc.pyx

@@ -1602,13 +1602,30 @@ cdef class Doc:
                 ents.append(char_span)
             self.ents = ents
 
-        # Add custom attributes. Note that only Doc extensions are currently considered, Token and Span extensions are
+        # Add custom attributes for the whole Doc object.
-        # not yet supported.
         for attr in doc_json.get("_", {}):
             if not Doc.has_extension(attr):
                 Doc.set_extension(attr)
             self._.set(attr, doc_json["_"][attr])
 
+        if doc_json.get("underscore_token", {}):
+            for token_attr in doc_json["underscore_token"]:
+                token_start = doc_json["underscore_token"][token_attr]["token_start"]
+                value = doc_json["underscore_token"][token_attr]["value"]
+
+                if not Token.has_extension(token_attr):
+                    Token.set_extension(token_attr)
+                self[token_start]._.set(token_attr, value)
+                
+        if doc_json.get("underscore_span", {}):
+            for span_attr in doc_json["underscore_span"]:
+                token_start = doc_json["underscore_span"][span_attr]["token_start"]
+                token_end = doc_json["underscore_span"][span_attr]["token_end"]
+                value = doc_json["underscore_span"][span_attr]["value"]
+
+                if not Span.has_extension(span_attr):
+                    Span.set_extension(span_attr)
+                self[token_start:token_end]._.set(span_attr, value)
         return self
 
     def to_json(self, underscore=None):
@@ -1650,20 +1667,40 @@ cdef class Doc:
             for span_group in self.spans:
                 data["spans"][span_group] = []
                 for span in self.spans[span_group]:
-                    span_data = {
+                    span_data = {"start": span.start_char, "end": span.end_char, "label": span.label_, "kb_id": span.kb_id_}
-                        "start": span.start_char, "end": span.end_char, "label": span.label_, "kb_id": span.kb_id_
-                    }
                     data["spans"][span_group].append(span_data)
 
         if underscore:
-            data["_"] = {}
+            user_keys = set()
+            if self.user_data:
+                data["_"] = {}
+                data["underscore_token"] = {}
+                data["underscore_span"] = {}
+                for data_key in self.user_data:
+                    if type(data_key) == tuple and len(data_key) >= 4 and data_key[0] == "._.":
+                        attr = data_key[1]
+                        start = data_key[2]
+                        end = data_key[3]
+                        if attr in underscore:
+                            user_keys.add(attr)
+                            value = self.user_data[data_key]
+                            if not srsly.is_json_serializable(value):
+                                raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
+                            # Check if doc attribute
+                            if start is None:
+                                data["_"][attr] = value
+                            # Check if token attribute
+                            elif end is None:
+                                if attr not in data["underscore_token"]:
+                                    data["underscore_token"][attr] = {"token_start": start, "value": value}
+                            # Else span attribute
+                            else:
+                                if attr not in data["underscore_span"]:
+                                    data["underscore_span"][attr] = {"token_start": start, "token_end": end, "value": value}
+
             for attr in underscore:
-                if not self.has_extension(attr):
+                if attr not in user_keys:
                     raise ValueError(Errors.E106.format(attr=attr, opts=underscore))
-                value = self._.get(attr)
-                if not srsly.is_json_serializable(value):
-                    raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
-                data["_"][attr] = value
         return data
 
     def to_utf8_array(self, int nr_char=-1):

spacy/training/example.pyx

@@ -361,6 +361,7 @@ cdef class Example:
             "doc_annotation": {
                 "cats": dict(self.reference.cats),
                 "entities": doc_to_biluo_tags(self.reference),
+                "spans": self._spans_to_dict(),
                 "links": self._links_to_dict()
             },
             "token_annotation": {
@@ -376,6 +377,18 @@ cdef class Example:
             }
         }
 
+    def _spans_to_dict(self):
+        span_dict = {}
+        for key in self.reference.spans:
+            span_tuples = []
+            for span in self.reference.spans[key]: 
+                span_tuple = (span.start_char, span.end_char, span.label_, span.kb_id_)
+                span_tuples.append(span_tuple)
+            span_dict[key] = span_tuples
+
+        return span_dict
+
+
     def _links_to_dict(self):
         links = {}
         for ent in self.reference.ents:

spacy/training/initialize.py

@@ -337,3 +337,5 @@ def ensure_shape(vectors_loc):
         # store all the results in a list in memory
         lines2 = open_file(vectors_loc)
         yield from lines2
+        lines2.close()
+    lines.close()

website/docs/api/data-formats.md

@@ -395,12 +395,13 @@ file to keep track of your settings and hyperparameters and your own
 >    "pos": List[str],
 >    "morphs": List[str],
 >    "sent_starts": List[Optional[bool]],
->    "deps": List[string],
+>    "deps": List[str],
 >    "heads": List[int],
 >    "entities": List[str],
 >    "entities": List[(int, int, str)],
 >    "cats": Dict[str, float],
 >    "links": Dict[(int, int), dict],
+>    "spans": Dict[str, List[Tuple]],
 > }
 > ```
 
@@ -417,9 +418,10 @@ file to keep track of your settings and hyperparameters and your own
 | `deps`        | List of string values indicating the [dependency relation](/usage/linguistic-features#dependency-parse) of a token to its head. ~~List[str]~~                                                                                  |
 | `heads`       | List of integer values indicating the dependency head of each token, referring to the absolute index of each token in the text. ~~List[int]~~                                                                                  |
 | `entities`    | **Option 1:** List of [BILUO tags](/usage/linguistic-features#accessing-ner) per token of the format `"{action}-{label}"`, or `None` for unannotated tokens. ~~List[str]~~                                                     |
-| `entities`    | **Option 2:** List of `"(start, end, label)"` tuples defining all entities in the text. ~~List[Tuple[int, int, str]]~~                                                                                                         |
+| `entities`    | **Option 2:** List of `(start_char, end_char, label)` tuples defining all entities in the text. ~~List[Tuple[int, int, str]]~~                                                                                                 |
 | `cats`        | Dictionary of `label`/`value` pairs indicating how relevant a certain [text category](/api/textcategorizer) is for the text. ~~Dict[str, float]~~                                                                              |
 | `links`       | Dictionary of `offset`/`dict` pairs defining [named entity links](/usage/linguistic-features#entity-linking). The character offsets are linked to a dictionary of relevant knowledge base IDs. ~~Dict[Tuple[int, int], Dict]~~ |
+| `spans`       | Dictionary of `spans_key`/`List[Tuple]` pairs defining the spans for each spans key as `(start_char, end_char, label, kb_id)` tuples. ~~Dict[str, List[Tuple[int, int, str, str]]~~                                            |
 
 <Infobox title="Notes and caveats">
 

website/docs/api/top-level.md

@@ -240,7 +240,7 @@ browser. Will run a simple web server.
 | Name      | Description                                                                                                                                                       |
 | --------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `docs`    | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span]], Doc, Span]~~                                                                             |
-| `style`   | Visualization style, `"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~                                                                                             |
+| `style`   | Visualization style, `"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~                                                      |
 | `page`    | Render markup as full HTML page. Defaults to `True`. ~~bool~~                                                                                                     |
 | `minify`  | Minify HTML markup. Defaults to `False`. ~~bool~~                                                                                                                 |
 | `options` | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~                                                                                 |
@@ -265,7 +265,7 @@ Render a dependency parse tree or named entity visualization.
 | Name        | Description                                                                                                                                                                            |
 | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `docs`      | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span, dict]], Doc, Span, dict]~~                                                                                      |
-| `style`     | Visualization style,`"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~                                                                                                                  |
+| `style`     | Visualization style, `"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~                                                                           |
 | `page`      | Render markup as full HTML page. Defaults to `True`. ~~bool~~                                                                                                                          |
 | `minify`    | Minify HTML markup. Defaults to `False`. ~~bool~~                                                                                                                                      |
 | `options`   | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~                                                                                                      |
@@ -273,6 +273,73 @@ Render a dependency parse tree or named entity visualization.
 | `jupyter`   | Explicitly enable or disable "[Jupyter](http://jupyter.org/) mode" to return markup ready to be rendered in a notebook. Detected automatically if `None` (default). ~~Optional[bool]~~ |
 | **RETURNS** | The rendered HTML markup. ~~str~~                                                                                                                                                      |
 
+### displacy.parse_deps {#displacy.parse_deps tag="method" new="2"}
+
+Generate dependency parse in `{'words': [], 'arcs': []}` format.
+For use with the `manual=True` argument in `displacy.render`.
+
+> #### Example
+>
+> ```python
+> import spacy
+> from spacy import displacy
+> nlp = spacy.load("en_core_web_sm")
+> doc = nlp("This is a sentence.")
+> deps_parse = displacy.parse_deps(doc)
+> html = displacy.render(deps_parse, style="dep", manual=True)
+> ```
+
+| Name        | Description                                                         |
+| ----------- | ------------------------------------------------------------------- |
+| `orig_doc`  | Doc to parse dependencies. ~~Doc~~                                  |
+| `options`   | Dependency parse specific visualisation options. ~~Dict[str, Any]~~ |
+| **RETURNS** | Generated dependency parse keyed by words and arcs. ~~dict~~        |
+
+### displacy.parse_ents {#displacy.parse_ents tag="method" new="2"}
+
+Generate named entities in `[{start: i, end: i, label: 'label'}]` format.
+For use with the `manual=True` argument in `displacy.render`.
+
+> #### Example
+>
+> ```python
+> import spacy
+> from spacy import displacy
+> nlp = spacy.load("en_core_web_sm")
+> doc = nlp("But Google is starting from behind.")
+> ents_parse = displacy.parse_ents(doc)
+> html = displacy.render(ents_parse, style="ent", manual=True)
+> ```
+
+| Name        | Description                                                         |
+| ----------- | ------------------------------------------------------------------- |
+| `doc`       | Doc to parse entities. ~~Doc~~                                      |
+| `options`   | NER-specific visualisation options. ~~Dict[str, Any]~~              |
+| **RETURNS** | Generated entities keyed by text (original text) and ents. ~~dict~~ |
+
+### displacy.parse_spans {#displacy.parse_spans tag="method" new="2"}
+
+Generate spans in `[{start_token: i, end_token: i, label: 'label'}]` format.
+For use with the `manual=True` argument in `displacy.render`.
+
+> #### Example
+>
+> ```python
+> import spacy
+> from spacy import displacy
+> nlp = spacy.load("en_core_web_sm")
+> doc = nlp("But Google is starting from behind.")
+> doc.spans['orgs'] = [doc[1:2]]
+> ents_parse = displacy.parse_spans(doc, options={"spans_key" : "orgs"})
+> html = displacy.render(ents_parse, style="span", manual=True)
+> ```
+
+| Name        | Description                                                         |
+| ----------- | ------------------------------------------------------------------- |
+| `doc`       | Doc to parse entities. ~~Doc~~                                      |
+| `options`   | Span-specific visualisation options. ~~Dict[str, Any]~~             |
+| **RETURNS** | Generated entities keyed by text (original text) and ents. ~~dict~~ |
+
 ### Visualizer options {#displacy_options}
 
 The `options` argument lets you specify additional settings for each visualizer.

website/docs/usage/linguistic-features.md

@@ -11,8 +11,8 @@ menu:
   - ['Tokenization', 'tokenization']
   - ['Merging & Splitting', 'retokenization']
   - ['Sentence Segmentation', 'sbd']
-  - ['Vectors & Similarity', 'vectors-similarity']
   - ['Mappings & Exceptions', 'mappings-exceptions']
+  - ['Vectors & Similarity', 'vectors-similarity']
   - ['Language Data', 'language-data']
 ---
 

website/docs/usage/visualizers.md

@@ -198,12 +198,12 @@ import DisplacySpanHtml from 'images/displacy-span.html'
 
 The span visualizer lets you customize the following `options`:
 
-| Argument | Description                                                                                                                                             |
+| Argument          | Description                                                                                                                                                                               |
-|-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|
+| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `spans_key`       | Which spans key to render spans from. Default is `"sc"`. ~~str~~                                                                                                   |
+| `spans_key`       | Which spans key to render spans from. Default is `"sc"`. ~~str~~                                                                                                                          |
 | `templates`       | Dictionary containing the keys `"span"`, `"slice"`, and `"start"`. These dictate how the overall span, a span slice, and the starting token will be rendered. ~~Optional[Dict[str, str]~~ |
-| `kb_url_template` | Optional template to construct the KB url for the entity to link to. Expects a python f-string format with single field to fill in ~~Optional[str]~~                    |
+| `kb_url_template` | Optional template to construct the KB url for the entity to link to. Expects a python f-string format with single field to fill in ~~Optional[str]~~                                      |
-| `colors`          | Color overrides. Entity types should be mapped to color names or values. ~~Dict[str, str]~~ |
+| `colors`          | Color overrides. Entity types should be mapped to color names or values. ~~Dict[str, str]~~                                                                                               |
 
 Because spans can be stored across different keys in `doc.spans`, you need to specify
 which one displaCy should use with `spans_key` (`sc` is the default). 
@@ -343,9 +343,21 @@ want to visualize output from other libraries, like [NLTK](http://www.nltk.org)
 or
 [SyntaxNet](https://github.com/tensorflow/models/tree/master/research/syntaxnet).
 If you set `manual=True` on either `render()` or `serve()`, you can pass in data
-in displaCy's format as a dictionary (instead of `Doc` objects).
+in displaCy's format as a dictionary (instead of `Doc` objects). There are helper 
+functions for converting `Doc` objects to displaCy's format for use with `manual=True`: 
+[`displacy.parse_deps`](/api/top-level#displacy.parse_deps), 
+[`displacy.parse_ents`](/api/top-level#displacy.parse_ents), 
+and [`displacy.parse_spans`](/api/top-level#displacy.parse_spans).
 
-> #### Example
+> #### Example with parse function
+>
+> ```python
+> doc = nlp("But Google is starting from behind.")
+> ex = displacy.parse_ents(doc)
+> html = displacy.render(ex, style="ent", manual=True)
+> ```
+
+> #### Example with raw data
 >
 > ```python
 > ex = [{"text": "But Google is starting from behind.",
@@ -354,6 +366,7 @@ in displaCy's format as a dictionary (instead of `Doc` objects).
 > html = displacy.render(ex, style="ent", manual=True)
 > ```
 
+
 ```python
 ### DEP input
 {
@@ -389,6 +402,18 @@ in displaCy's format as a dictionary (instead of `Doc` objects).
 }
 ```
 
+```python
+### SPANS input
+{
+    "text": "Welcome to the Bank of China.",
+    "spans": [
+        {"start_token": 3, "end_token": 6, "label": "ORG"},
+        {"start_token": 5, "end_token": 6, "label": "GPE"},
+    ],
+    "tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."],
+}
+```
+
 ## Using displaCy in a web application {#webapp}
 
 If you want to use the visualizers as part of a web application, for example to

website/meta/languages.json

@@ -265,6 +265,11 @@
             "name": "Luxembourgish",
             "has_examples": true
         },
+        {
+            "code": "lg",
+            "name": "Luganda",
+            "has_examples": true
+        },
         {
             "code": "lij",
             "name": "Ligurian",
@@ -467,10 +472,20 @@
             "code": "uk",
             "name": "Ukrainian",
             "has_examples": true,
+            "models": [
+                "uk_core_news_sm",
+                "uk_core_news_md",
+                "uk_core_news_lg",
+                "uk_core_news_trf"
+            ],
             "dependencies": [
                 {
                     "name": "pymorphy3",
                     "url": "https://github.com/no-plagiarism/pymorphy3"
+                },
+                {
+                    "name": "pymorphy3-dicts-uk",
+                    "url": "https://github.com/no-plagiarism/pymorphy3-dicts"
                 }
             ]
         },

website/src/templates/models.js

@@ -114,7 +114,11 @@ function formatVectors(data) {
     if (!data) return 'n/a'
     if (Object.values(data).every(n => n === 0)) return 'context vectors only'
     const { keys, vectors, width } = data
-    return `${abbrNum(keys)} keys, ${abbrNum(vectors)} unique vectors (${width} dimensions)`
+    if (keys >= 0) {
+        return `${abbrNum(keys)} keys, ${abbrNum(vectors)} unique vectors (${width} dimensions)`
+    } else {
+        return `${abbrNum(vectors)} floret vectors (${width} dimensions)`
+    }
 }
 
 function formatAccuracy(data, lang) {