Merge pull request #13490 from svlandeg/feat/update_v4

Update v4 branch with latest from master

.github/workflows/explosionbot.yml

@@ -15,7 +15,7 @@ jobs:
         env:
           GITHUB_CONTEXT: ${{ toJson(github) }}
         run: echo "$GITHUB_CONTEXT"
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
       - name: Install and run explosion-bot
         run: |

.github/workflows/gputests.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        branch: [master, main]
+        branch: [master, v4]
     if: github.repository_owner == 'explosion'
     runs-on: ubuntu-latest
     steps:

.github/workflows/lock.yml

@@ -16,7 +16,7 @@ jobs:
     if: github.repository_owner == 'explosion'
     runs-on: ubuntu-latest
     steps:
-      - uses: dessant/lock-threads@v4
+      - uses: dessant/lock-threads@v5
         with:
           process-only: 'issues'
           issue-inactive-days: '30'

.github/workflows/slowtests.yml

@@ -9,12 +9,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        branch: [master, main]
+        branch: [master, v4]
     if: github.repository_owner == 'explosion'
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           ref: ${{ matrix.branch }}
       - name: Get commits from past 24 hours

.github/workflows/spacy_universe_alert.yml

@@ -18,7 +18,7 @@ jobs:
         run: |
           echo "$GITHUB_CONTEXT"
 
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
           python-version: '3.10'

.github/workflows/tests.yml

@@ -25,13 +25,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Configure Python version
         uses: actions/setup-python@v4
         with:
           python-version: "3.9"
-          architecture: x64
 
       - name: black
         run: |
@@ -71,13 +70,12 @@ jobs:
 
     steps:
       - name: Check out repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Configure Python version
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python_version }}
-          architecture: x64
 
       - name: Install dependencies
         run: |

.github/workflows/universe_validation.yml

@@ -20,13 +20,12 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out repo
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Configure Python version
         uses: actions/setup-python@v4
         with:
           python-version: "3.9"
-          architecture: x64
 
       - name: Validate website/meta/universe.json
         run: |

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (C) 2016-2023 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal
+Copyright (C) 2016-2024 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

requirements.txt

@@ -9,9 +9,8 @@ murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0
 srsly>=2.4.3,<3.0.0
 catalogue>=2.0.6,<2.1.0
-typer>=0.3.0,<0.10.0
+typer>=0.3.0,<1.0.0
-smart-open>=5.2.1,<7.0.0
+weasel>=0.1.0,<0.5.0
-weasel>=0.1.0,<0.4.0
 # Third party dependencies
 numpy>=1.15.0; python_version < "3.9"
 numpy>=1.19.0; python_version >= "3.9"

setup.cfg

@@ -41,10 +41,9 @@ install_requires =
     wasabi>=0.9.1,<1.2.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0
-    weasel>=0.1.0,<0.4.0
+    weasel>=0.1.0,<0.5.0
     # Third-party dependencies
-    typer>=0.3.0,<0.10.0
+    typer>=0.3.0,<1.0.0
-    smart-open>=5.2.1,<7.0.0
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0; python_version < "3.9"
     numpy>=1.19.0; python_version >= "3.9"

spacy/cli/__init__.py

@@ -1,5 +1,7 @@
 from wasabi import msg
 
+# Needed for testing
+from . import download as download_module  # noqa: F401
 from ._util import app, setup_cli  # noqa: F401
 from .apply import apply  # noqa: F401
 from .assemble import assemble_cli  # noqa: F401

spacy/cli/download.py

@@ -1,5 +1,6 @@
 import sys
 from typing import Optional, Sequence
+from urllib.parse import urljoin
 
 import requests
 import typer
@@ -64,6 +65,13 @@ def download(
         )
         pip_args = pip_args + ("--no-deps",)
     if direct:
+        # Reject model names with '/', in order to prevent shenanigans.
+        if "/" in model:
+            msg.fail(
+                title="Model download rejected",
+                text=f"Cannot download model '{model}'. Models are expected to be file names, not URLs or fragments",
+                exits=True,
+            )
         components = model.split("-")
         model_name = "".join(components[:-1])
         version = components[-1]
@@ -156,7 +164,16 @@ def get_latest_version(model: str) -> str:
 def download_model(
     filename: str, user_pip_args: Optional[Sequence[str]] = None
 ) -> None:
-    download_url = about.__download_url__ + "/" + filename
+    # Construct the download URL carefully. We need to make sure we don't
+    # allow relative paths or other shenanigans to trick us into download
+    # from outside our own repo.
+    base_url = about.__download_url__
+    # urljoin requires that the path ends with /, or the last path part will be dropped
+    if not base_url.endswith("/"):
+        base_url = about.__download_url__ + "/"
+    download_url = urljoin(base_url, filename)
+    if not download_url.startswith(about.__download_url__):
+        raise ValueError(f"Download from {filename} rejected. Was it a relative path?")
     pip_args = list(user_pip_args) if user_pip_args is not None else []
     cmd = [sys.executable, "-m", "pip", "install"] + pip_args + [download_url]
     run_command(cmd)

spacy/cli/find_threshold.py

@@ -39,7 +39,7 @@ def find_threshold_cli(
     # fmt: on
 ):
     """
-    Runs prediction trials for a trained model with varying tresholds to maximize
+    Runs prediction trials for a trained model with varying thresholds to maximize
     the specified metric. The search space for the threshold is traversed linearly
     from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
     (the corresponding API call to `spacy.cli.find_threshold.find_threshold()`
@@ -81,7 +81,7 @@ def find_threshold(
     silent: bool = True,
 ) -> Tuple[float, float, Dict[float, float]]:
     """
-    Runs prediction trials for models with varying tresholds to maximize the specified metric.
+    Runs prediction trials for models with varying thresholds to maximize the specified metric.
     model (Union[str, Path]): Pipeline to evaluate. Can be a package or a path to a data directory.
     data_path (Path): Path to file with DocBin with docs to use for threshold search.
     pipe_name (str): Name of pipe to examine thresholds for.

spacy/errors.py

@@ -215,6 +215,7 @@ class Warnings(metaclass=ErrorsWithCodes):
             "key attribute for vectors, configure it through Vectors(attr=) or "
             "'spacy init vectors --attr'")
     W126 = ("These keys are unsupported: {unsupported}")
+    W127 = ("Not all `Language.pipe` worker processes completed successfully")
 
     # v4 warning strings
     W401 = ("`incl_prior is True`, but the selected knowledge base type {kb_type} doesn't support prior probability "

spacy/language.py

@@ -1844,6 +1844,9 @@ class Language:
             for proc in procs:
                 proc.join()
 
+            if not all(proc.exitcode == 0 for proc in procs):
+                warnings.warn(Warnings.W127)
+
     def _link_components(self) -> None:
         """Register 'listeners' within pipeline components, to allow them to
         effectively share weights.
@@ -2467,6 +2470,7 @@ def _apply_pipes(
             if isinstance(texts_with_ctx, _WorkDoneSentinel):
                 sender.close()
                 receiver.close()
+                return
 
             docs = (
                 ensure_doc(doc_like, context) for doc_like, context in texts_with_ctx
@@ -2492,6 +2496,7 @@ def _apply_pipes(
             # stop processing.
             sender.close()
             receiver.close()
+            return
 
 
 class _Sender:

spacy/lexeme.pyx

@@ -164,31 +164,33 @@ cdef class Lexeme:
         vector = self.vector
         return numpy.sqrt((vector**2).sum())
 
-    property vector:
+    @property
+    def vector(self):
         """A real-valued meaning representation.
 
         RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
             representing the lexeme's semantics.
         """
-        def __get__(self):
         cdef int length = self.vocab.vectors_length
         if length == 0:
             raise ValueError(Errors.E010)
         return self.vocab.get_vector(self.c.orth)
 
-        def __set__(self, vector):
+    @vector.setter
+    def vector(self, vector):
         if len(vector) != self.vocab.vectors_length:
             raise ValueError(Errors.E073.format(new_length=len(vector),
                                                 length=self.vocab.vectors_length))
         self.vocab.set_vector(self.c.orth, vector)
 
-    property rank:
+    @property
+    def rank(self):
         """RETURNS (str): Sequential ID of the lexeme's lexical type, used
             to index into tables, e.g. for word vectors."""
-        def __get__(self):
         return self.c.id
 
-        def __set__(self, value):
+    @rank.setter
+    def rank(self, value):
         self.c.id = value
 
     @property
@@ -203,151 +205,166 @@ cdef class Lexeme:
         """RETURNS (str): The original verbatim text of the lexeme."""
         return self.orth_
 
-    property lower:
+    @property
+    def lower(self):
         """RETURNS (uint64): Lowercase form of the lexeme."""
-        def __get__(self):
         return self.c.lower
 
-        def __set__(self, attr_t x):
+    @lower.setter
+    def lower(self, attr_t x):
         self.c.lower = x
 
-    property norm:
+    @property
+    def norm(self):
         """RETURNS (uint64): The lexeme's norm, i.e. a normalised form of the
             lexeme text.
         """
-        def __get__(self):
         return self.c.norm
 
-        def __set__(self, attr_t x):
+    @norm.setter
+    def norm(self, attr_t x):
         if "lexeme_norm" not in self.vocab.lookups:
             self.vocab.lookups.add_table("lexeme_norm")
         norm_table = self.vocab.lookups.get_table("lexeme_norm")
         norm_table[self.c.orth] = self.vocab.strings[x]
         self.c.norm = x
 
-    property shape:
+    @property
+    def shape(self):
         """RETURNS (uint64): Transform of the word's string, to show
             orthographic features.
         """
-        def __get__(self):
         return self.c.shape
 
-        def __set__(self, attr_t x):
+    @shape.setter
+    def shape(self, attr_t x):
         self.c.shape = x
 
-    property prefix:
+    @property
+    def prefix(self):
         """RETURNS (uint64): Length-N substring from the start of the word.
             Defaults to `N=1`.
         """
-        def __get__(self):
         return self.c.prefix
 
-        def __set__(self, attr_t x):
+    @prefix.setter
+    def prefix(self, attr_t x):
         self.c.prefix = x
 
-    property suffix:
+    @property
+    def suffix(self):
         """RETURNS (uint64): Length-N substring from the end of the word.
             Defaults to `N=3`.
         """
-        def __get__(self):
         return self.c.suffix
 
-        def __set__(self, attr_t x):
+    @suffix.setter
+    def suffix(self, attr_t x):
         self.c.suffix = x
 
-    property cluster:
+    @property
+    def cluster(self):
         """RETURNS (int): Brown cluster ID."""
-        def __get__(self):
         cluster_table = self.vocab.lookups.get_table("lexeme_cluster", {})
         return cluster_table.get(self.c.orth, 0)
 
-        def __set__(self, int x):
+    @cluster.setter
+    def cluster(self, int x):
         cluster_table = self.vocab.lookups.get_table("lexeme_cluster", {})
         cluster_table[self.c.orth] = x
 
-    property lang:
+    @property
+    def lang(self):
         """RETURNS (uint64): Language of the parent vocabulary."""
-        def __get__(self):
         return self.c.lang
 
-        def __set__(self, attr_t x):
+    @lang.setter
+    def lang(self, attr_t x):
         self.c.lang = x
 
-    property prob:
+    @property
+    def prob(self):
         """RETURNS (float): Smoothed log probability estimate of the lexeme's
             type."""
-        def __get__(self):
         prob_table = self.vocab.lookups.get_table("lexeme_prob", {})
         settings_table = self.vocab.lookups.get_table("lexeme_settings", {})
         default_oov_prob = settings_table.get("oov_prob", -20.0)
         return prob_table.get(self.c.orth, default_oov_prob)
 
-        def __set__(self, float x):
+    @prob.setter
+    def prob(self, float x):
         prob_table = self.vocab.lookups.get_table("lexeme_prob", {})
         prob_table[self.c.orth] = x
 
-    property lower_:
+    @property
+    def lower_(self):
         """RETURNS (str): Lowercase form of the word."""
-        def __get__(self):
         return self.vocab.strings[self.c.lower]
 
-        def __set__(self, str x):
+    @lower_.setter
+    def lower_(self, str x):
         self.c.lower = self.vocab.strings.add(x)
 
-    property norm_:
+    @property
+    def norm_(self):
         """RETURNS (str): The lexeme's norm, i.e. a normalised form of the
             lexeme text.
         """
-        def __get__(self):
         return self.vocab.strings[self.c.norm]
 
-        def __set__(self, str x):
+    @norm_.setter
+    def norm_(self, str x):
         self.norm = self.vocab.strings.add(x)
 
-    property shape_:
+    @property
+    def shape_(self):
         """RETURNS (str): Transform of the word's string, to show
             orthographic features.
         """
-        def __get__(self):
         return self.vocab.strings[self.c.shape]
 
-        def __set__(self, str x):
+    @shape_.setter
+    def shape_(self, str x):
         self.c.shape = self.vocab.strings.add(x)
 
-    property prefix_:
+    @property
+    def prefix_(self):
         """RETURNS (str): Length-N substring from the start of the word.
             Defaults to `N=1`.
         """
-        def __get__(self):
         return self.vocab.strings[self.c.prefix]
 
-        def __set__(self, str x):
+    @prefix_.setter
+    def prefix_(self, str x):
         self.c.prefix = self.vocab.strings.add(x)
 
-    property suffix_:
+    @property
+    def suffix_(self):
         """RETURNS (str): Length-N substring from the end of the word.
             Defaults to `N=3`.
         """
-        def __get__(self):
         return self.vocab.strings[self.c.suffix]
 
-        def __set__(self, str x):
+    @suffix_.setter
+    def suffix_(self, str x):
         self.c.suffix = self.vocab.strings.add(x)
 
-    property lang_:
+    @property
+    def lang_(self):
         """RETURNS (str): Language of the parent vocabulary."""
-        def __get__(self):
         return self.vocab.strings[self.c.lang]
 
-        def __set__(self, str x):
+    @lang_.setter
+    def lang_(self, str x):
         self.c.lang = self.vocab.strings.add(x)
 
-    property flags:
+    @property
+    def flags(self):
         """RETURNS (uint64): Container of the lexeme's binary flags."""
-        def __get__(self):
         return self.c.flags
 
-        def __set__(self, flags_t x):
+    @flags.setter
+    def flags(self, flags_t x):
         self.c.flags = x
 
     @property
@@ -355,154 +372,171 @@ cdef class Lexeme:
         """RETURNS (bool): Whether the lexeme is out-of-vocabulary."""
         return self.orth not in self.vocab.vectors
 
-    property is_stop:
+    @property
+    def is_stop(self):
         """RETURNS (bool): Whether the lexeme is a stop word."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_STOP)
 
-        def __set__(self, bint x):
+    @is_stop.setter
+    def is_stop(self, bint x):
         Lexeme.c_set_flag(self.c, IS_STOP, x)
 
-    property is_alpha:
+    @property
+    def is_alpha(self):
         """RETURNS (bool): Whether the lexeme consists of alphabetic
             characters. Equivalent to `lexeme.text.isalpha()`.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_ALPHA)
 
-        def __set__(self, bint x):
+    @is_alpha.setter
+    def is_alpha(self, bint x):
         Lexeme.c_set_flag(self.c, IS_ALPHA, x)
 
-    property is_ascii:
+    @property
+    def is_ascii(self):
         """RETURNS (bool): Whether the lexeme consists of ASCII characters.
             Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_ASCII)
 
-        def __set__(self, bint x):
+    @is_ascii.setter
+    def is_ascii(self, bint x):
         Lexeme.c_set_flag(self.c, IS_ASCII, x)
 
-    property is_digit:
+    @property
+    def is_digit(self):
         """RETURNS (bool): Whether the lexeme consists of digits. Equivalent
             to `lexeme.text.isdigit()`.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_DIGIT)
 
-        def __set__(self, bint x):
+    @is_digit.setter
+    def is_digit(self, bint x):
         Lexeme.c_set_flag(self.c, IS_DIGIT, x)
 
-    property is_lower:
+    @property
+    def is_lower(self):
         """RETURNS (bool): Whether the lexeme is in lowercase. Equivalent to
             `lexeme.text.islower()`.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_LOWER)
 
-        def __set__(self, bint x):
+    @is_lower.setter
+    def is_lower(self, bint x):
         Lexeme.c_set_flag(self.c, IS_LOWER, x)
 
-    property is_upper:
+    @property
+    def is_upper(self):
         """RETURNS (bool): Whether the lexeme is in uppercase. Equivalent to
             `lexeme.text.isupper()`.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_UPPER)
 
-        def __set__(self, bint x):
+    @is_upper.setter
+    def is_upper(self, bint x):
         Lexeme.c_set_flag(self.c, IS_UPPER, x)
 
-    property is_title:
+    @property
+    def is_title(self):
         """RETURNS (bool): Whether the lexeme is in titlecase. Equivalent to
             `lexeme.text.istitle()`.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_TITLE)
 
-        def __set__(self, bint x):
+    @is_title.setter
+    def is_title(self, bint x):
         Lexeme.c_set_flag(self.c, IS_TITLE, x)
 
-    property is_punct:
+    @property
+    def is_punct(self):
         """RETURNS (bool): Whether the lexeme is punctuation."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_PUNCT)
 
-        def __set__(self, bint x):
+    @is_punct.setter
+    def is_punct(self, bint x):
         Lexeme.c_set_flag(self.c, IS_PUNCT, x)
 
-    property is_space:
+    @property
+    def is_space(self):
         """RETURNS (bool): Whether the lexeme consist of whitespace characters.
             Equivalent to `lexeme.text.isspace()`.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_SPACE)
 
-        def __set__(self, bint x):
+    @is_space.setter
+    def is_space(self, bint x):
         Lexeme.c_set_flag(self.c, IS_SPACE, x)
 
-    property is_bracket:
+    @property
+    def is_bracket(self):
         """RETURNS (bool): Whether the lexeme is a bracket."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_BRACKET)
 
-        def __set__(self, bint x):
+    @is_bracket.setter
+    def is_bracket(self, bint x):
         Lexeme.c_set_flag(self.c, IS_BRACKET, x)
 
-    property is_quote:
+    @property
+    def is_quote(self):
         """RETURNS (bool): Whether the lexeme is a quotation mark."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_QUOTE)
 
-        def __set__(self, bint x):
+    @is_quote.setter
+    def is_quote(self, bint x):
         Lexeme.c_set_flag(self.c, IS_QUOTE, x)
 
-    property is_left_punct:
+    @property
+    def is_left_punct(self):
         """RETURNS (bool): Whether the lexeme is left punctuation, e.g. (."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_LEFT_PUNCT)
 
-        def __set__(self, bint x):
+    @is_left_punct.setter
+    def is_left_punct(self, bint x):
         Lexeme.c_set_flag(self.c, IS_LEFT_PUNCT, x)
 
-    property is_right_punct:
+    @property
+    def is_right_punct(self):
         """RETURNS (bool): Whether the lexeme is right punctuation, e.g. )."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_RIGHT_PUNCT)
 
-        def __set__(self, bint x):
+    @is_right_punct.setter
+    def is_right_punct(self, bint x):
         Lexeme.c_set_flag(self.c, IS_RIGHT_PUNCT, x)
 
-    property is_currency:
+    @property
+    def is_currency(self):
         """RETURNS (bool): Whether the lexeme is a currency symbol, e.g. $, €."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, IS_CURRENCY)
 
-        def __set__(self, bint x):
+    @is_currency.setter
+    def is_currency(self, bint x):
         Lexeme.c_set_flag(self.c, IS_CURRENCY, x)
 
-    property like_url:
+    @property
+    def like_url(self):
         """RETURNS (bool): Whether the lexeme resembles a URL."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, LIKE_URL)
 
-        def __set__(self, bint x):
+    @like_url.setter
+    def like_url(self, bint x):
         Lexeme.c_set_flag(self.c, LIKE_URL, x)
 
-    property like_num:
+    @property
+    def like_num(self):
         """RETURNS (bool): Whether the lexeme represents a number, e.g. "10.9",
             "10", "ten", etc.
         """
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, LIKE_NUM)
 
-        def __set__(self, bint x):
+    @like_num.setter
+    def like_num(self, bint x):
         Lexeme.c_set_flag(self.c, LIKE_NUM, x)
 
-    property like_email:
+    @property
+    def like_email(self):
         """RETURNS (bool): Whether the lexeme resembles an email address."""
-        def __get__(self):
         return Lexeme.c_check_flag(self.c, LIKE_EMAIL)
 
-        def __set__(self, bint x):
+    @like_email.setter
+    def like_email(self, bint x):
         Lexeme.c_set_flag(self.c, LIKE_EMAIL, x)

spacy/ml/models/textcat.py

@@ -241,6 +241,7 @@ def _build_parametric_attention_with_residual_nonlinear(
 
         parametric_attention.set_ref("tok2vec", tok2vec)
         parametric_attention.set_ref("attention_layer", attention_layer)
+        parametric_attention.set_ref("key_transform", key_transform)
         parametric_attention.set_ref("nonlinear_layer", nonlinear_layer)
         parametric_attention.set_ref("norm_layer", norm_layer)
 
@@ -248,10 +249,19 @@ def _build_parametric_attention_with_residual_nonlinear(
 
 
 def _init_parametric_attention_with_residual_nonlinear(model, X, Y) -> Model:
+    # When tok2vec is lazily initialized, we need to initialize it before
+    # the rest of the chain to ensure that we can get its width.
+    tok2vec = model.get_ref("tok2vec")
+    tok2vec.initialize(X)
+
     tok2vec_width = get_tok2vec_width(model)
     model.get_ref("attention_layer").set_dim("nO", tok2vec_width)
-    model.get_ref("nonlinear_layer").set_dim("nO", tok2vec_width)
+    if model.get_ref("key_transform").has_dim("nI") is None:
+        model.get_ref("key_transform").set_dim("nI", tok2vec_width)
+    if model.get_ref("key_transform").has_dim("nO") is None:
+        model.get_ref("key_transform").set_dim("nO", tok2vec_width)
     model.get_ref("nonlinear_layer").set_dim("nI", tok2vec_width)
+    model.get_ref("nonlinear_layer").set_dim("nO", tok2vec_width)
     model.get_ref("norm_layer").set_dim("nI", tok2vec_width)
     model.get_ref("norm_layer").set_dim("nO", tok2vec_width)
     init_chain(model, X, Y)

spacy/pipeline/entity_linker.py

@@ -24,7 +24,6 @@ from .. import util
 from ..errors import Errors, Warnings
 from ..kb import Candidate, KnowledgeBase
 from ..language import Language
-from ..ml import empty_kb
 from ..scorer import Scorer
 from ..tokens import Doc, Span, SpanGroup
 from ..training import Example, validate_examples, validate_get_examples
@@ -114,7 +113,7 @@ def make_entity_linker(
         documents with textual mentions.
     generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
     scorer (Optional[Callable]): The scoring method.
-    use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
+    use_gold_ents (bool): Whether to copy entities from gold docs during training or not. If false, another
         component must provide entity annotations.
     threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
         prediction is discarded. If None, predictions are not filtered by any threshold.
@@ -227,7 +226,6 @@ class EntityLinker(TrainablePipe):
         self.cfg: Dict[str, Any] = {"overwrite": overwrite}
         self.distance = CosineDistance(normalize=False)
         self.kb = generate_empty_kb(self.vocab, entity_vector_length)
-        self.scorer = scorer
         self.use_gold_ents = use_gold_ents
         self.threshold = threshold
         self.save_activations = save_activations
@@ -235,6 +233,37 @@ class EntityLinker(TrainablePipe):
         if self.incl_prior and not self.kb.supports_prior_probs:
             warnings.warn(Warnings.W401)
 
+        def _score_with_ents_set(examples: Iterable[Example], **kwargs):
+            # Because of how spaCy works, we can't just score immediately, because Language.evaluate
+            # calls pipe() on the predicted docs, which won't have entities if there is no NER in the pipeline.
+            if not scorer:
+                return scorer
+            if not self.use_gold_ents:
+                return scorer(examples, **kwargs)
+            else:
+                examples = self._ensure_ents(examples)
+                docs = self.pipe(
+                    (eg.predicted for eg in examples),
+                )
+                for eg, doc in zip(examples, docs):
+                    eg.predicted = doc
+                return scorer(examples, **kwargs)
+
+        self.scorer = _score_with_ents_set
+
+    def _ensure_ents(self, examples: Iterable[Example]) -> Iterable[Example]:
+        """If use_gold_ents is true, set the gold entities to (a copy of) eg.predicted."""
+        if not self.use_gold_ents:
+            return examples
+
+        new_examples = []
+        for eg in examples:
+            ents, _ = eg.get_aligned_ents_and_ner()
+            new_eg = eg.copy()
+            new_eg.predicted.ents = ents
+            new_examples.append(new_eg)
+        return new_examples
+
     def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
         """Define the KB of this pipe by providing a function that will
         create it using this object's vocab."""
@@ -276,11 +305,9 @@ class EntityLinker(TrainablePipe):
         nO = self.kb.entity_vector_length
         doc_sample = []
         vector_sample = []
-        for eg in islice(get_examples(), 10):
+        examples = self._ensure_ents(islice(get_examples(), 10))
+        for eg in examples:
             doc = eg.x
-            if self.use_gold_ents:
-                ents, _ = eg.get_aligned_ents_and_ner()
-                doc.ents = ents
             doc_sample.append(doc)
             vector_sample.append(self.model.ops.alloc1f(nO))
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
@@ -347,31 +374,17 @@ class EntityLinker(TrainablePipe):
         losses.setdefault(self.name, 0.0)
         if not examples:
             return losses
+        examples = self._ensure_ents(examples)
         validate_examples(examples, "EntityLinker.update")
 
-        set_dropout_rate(self.model, drop)
-        docs = [eg.predicted for eg in examples]
-        # save to restore later
-        old_ents = [doc.ents for doc in docs]
-
-        for doc, ex in zip(docs, examples):
-            if self.use_gold_ents:
-                ents, _ = ex.get_aligned_ents_and_ner()
-                doc.ents = ents
-            else:
-                # only keep matching ents
-                doc.ents = ex.get_matching_ents()
-
         # make sure we have something to learn from, if not, short-circuit
         if not self.batch_has_learnable_example(examples):
             return losses
 
+        set_dropout_rate(self.model, drop)
+        docs = [eg.predicted for eg in examples]
         sentence_encodings, bp_context = self.model.begin_update(docs)
 
-        # now restore the ents
-        for doc, old in zip(docs, old_ents):
-            doc.ents = old
-
         loss, d_scores = self.get_loss(
             sentence_encodings=sentence_encodings, examples=examples
         )
@@ -379,11 +392,13 @@ class EntityLinker(TrainablePipe):
         if sgd is not None:
             self.finish_update(sgd)
         losses[self.name] += loss
+
         return losses
 
     def get_loss(self, examples: Iterable[Example], sentence_encodings: Floats2d):
         validate_examples(examples, "EntityLinker.get_loss")
         entity_encodings = []
+        # We assume that get_loss is called with gold ents set in the examples if need be
         eidx = 0  # indices in gold entities to keep
         keep_ents = []  # indices in sentence_encodings to keep
 

spacy/tests/pipeline/test_entity_linker.py

@@ -799,7 +799,7 @@ GOLD_entities = ["Q2146908", "Q7381115", "Q7381115", "Q2146908"]
 # fmt: on
 
 
-def test_overfitting_IO():
+def test_overfitting_IO_gold_entities():
     # Simple test to try and quickly overfit the NEL component - ensuring the ML models work correctly
     nlp = English()
     vector_length = 3
@@ -826,7 +826,9 @@ def test_overfitting_IO():
         return mykb
 
     # Create the Entity Linker component and add it to the pipeline
-    entity_linker = nlp.add_pipe("entity_linker", last=True)
+    entity_linker = nlp.add_pipe(
+        "entity_linker", last=True, config={"use_gold_ents": True}
+    )
     assert isinstance(entity_linker, EntityLinker)
     entity_linker.set_kb(create_kb)
     assert "Q2146908" in entity_linker.vocab.strings
@@ -889,6 +891,107 @@ def test_overfitting_IO():
     assert_equal(batch_deps_1, batch_deps_2)
     assert_equal(batch_deps_1, no_batch_deps)
 
+    eval = nlp.evaluate(train_examples)
+    assert "nel_macro_p" in eval
+    assert "nel_macro_r" in eval
+    assert "nel_macro_f" in eval
+    assert "nel_micro_p" in eval
+    assert "nel_micro_r" in eval
+    assert "nel_micro_f" in eval
+    assert "nel_f_per_type" in eval
+    assert "PERSON" in eval["nel_f_per_type"]
+
+    assert eval["nel_macro_f"] > 0
+    assert eval["nel_micro_f"] > 0
+
+
+def test_overfitting_IO_with_ner():
+    # Simple test to try and overfit the NER and NEL component in combination - ensuring the ML models work correctly
+    nlp = English()
+    vector_length = 3
+    assert "Q2146908" not in nlp.vocab.strings
+
+    # Convert the texts to docs to make sure we have doc.ents set for the training examples
+    train_examples = []
+    for text, annotation in TRAIN_DATA:
+        doc = nlp(text)
+        train_examples.append(Example.from_dict(doc, annotation))
+
+    def create_kb(vocab):
+        # create artificial KB - assign same prior weight to the two russ cochran's
+        # Q2146908 (Russ Cochran): American golfer
+        # Q7381115 (Russ Cochran): publisher
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
+        mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
+        mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
+        mykb.add_alias(
+            alias="Russ Cochran",
+            entities=["Q2146908", "Q7381115"],
+            probabilities=[0.5, 0.5],
+        )
+        return mykb
+
+    # Create the NER and EL components and add them to the pipeline
+    ner = nlp.add_pipe("ner", first=True)
+    entity_linker = nlp.add_pipe(
+        "entity_linker", last=True, config={"use_gold_ents": False}
+    )
+    entity_linker.set_kb(create_kb)
+
+    train_examples = []
+    for text, annotations in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
+        for ent in annotations.get("entities"):
+            ner.add_label(ent[2])
+    optimizer = nlp.initialize()
+
+    # train the NER and NEL pipes
+    for i in range(50):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+    assert losses["ner"] < 0.001
+    assert losses["entity_linker"] < 0.001
+
+    # adding additional components that are required for the entity_linker
+    nlp.add_pipe("sentencizer", first=True)
+
+    # test the trained model
+    test_text = "Russ Cochran captured his first major title with his son as caddie."
+    doc = nlp(test_text)
+    ents = doc.ents
+    assert len(ents) == 1
+    assert ents[0].text == "Russ Cochran"
+    assert ents[0].label_ == "PERSON"
+    assert ents[0].kb_id_ != "NIL"
+
+    # TODO: below assert is still flaky - EL doesn't properly overfit quite yet
+    # assert ents[0].kb_id_ == "Q2146908"
+
+    # Also test the results are still the same after IO
+    with make_tempdir() as tmp_dir:
+        nlp.to_disk(tmp_dir)
+        nlp2 = util.load_model_from_path(tmp_dir)
+        assert nlp2.pipe_names == nlp.pipe_names
+        doc2 = nlp2(test_text)
+        ents2 = doc2.ents
+        assert len(ents2) == 1
+        assert ents2[0].text == "Russ Cochran"
+        assert ents2[0].label_ == "PERSON"
+        assert ents2[0].kb_id_ != "NIL"
+
+    eval = nlp.evaluate(train_examples)
+    assert "nel_macro_f" in eval
+    assert "nel_micro_f" in eval
+    assert "ents_f" in eval
+    assert "nel_f_per_type" in eval
+    assert "ents_per_type" in eval
+    assert "PERSON" in eval["nel_f_per_type"]
+    assert "PERSON" in eval["ents_per_type"]
+
+    assert eval["nel_macro_f"] > 0
+    assert eval["nel_micro_f"] > 0
+    assert eval["ents_f"] > 0
+
 
 def test_kb_serialization():
     # Test that the KB can be used in a pipeline with a different vocab

spacy/tests/pipeline/test_textcat.py

@@ -29,6 +29,8 @@ from spacy.tokens import Doc, DocBin
 from spacy.training import Example
 from spacy.training.initialize import init_nlp
 
+# Ensure that the architecture gets added to the registry.
+from ..tok2vec import build_lazy_init_tok2vec as _
 from ..util import make_tempdir
 
 TRAIN_DATA_SINGLE_LABEL = [
@@ -41,6 +43,13 @@ TRAIN_DATA_MULTI_LABEL = [
     ("I'm confused but happy", {"cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0}}),
 ]
 
+lazy_init_model_config = """
+[model]
+@architectures = "test.LazyInitTok2Vec.v1"
+width = 96
+"""
+LAZY_INIT_TOK2VEC_MODEL = Config().from_str(lazy_init_model_config)["model"]
+
 
 def make_get_examples_single_label(nlp):
     train_examples = []
@@ -551,6 +560,34 @@ def test_error_with_multi_labels():
         nlp.initialize(get_examples=lambda: train_examples)
 
 
+# fmt: off
+@pytest.mark.parametrize(
+    "name,textcat_config",
+    [
+        # ENSEMBLE V2
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
+        ("textcat", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
+        # PARAMETRIC ATTENTION V1
+        ("textcat", {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": True}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": False}),
+        # REDUCE
+        ("textcat", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
+    ],
+)
+# fmt: on
+def test_tok2vec_lazy_init(name, textcat_config):
+    # Check that we can properly initialize and use a textcat model using
+    # a lazily-initialized tok2vec.
+    nlp = English()
+    pipe_config = {"model": textcat_config}
+    textcat = nlp.add_pipe(name, config=pipe_config)
+    textcat.add_label("POSITIVE")
+    textcat.add_label("NEGATIVE")
+    nlp.initialize()
+    nlp.pipe(["This is a test."])
+
+
 @pytest.mark.parametrize(
     "name,get_examples, train_data",
     [

spacy/tests/test_cli.py

@@ -12,7 +12,7 @@ from thinc.api import Config
 
 import spacy
 from spacy import about
-from spacy.cli import info
+from spacy.cli import download_module, info
 from spacy.cli._util import parse_config_overrides, string_to_list, walk_directory
 from spacy.cli.apply import apply
 from spacy.cli.debug_data import (
@@ -1066,3 +1066,15 @@ def test_debug_data_trainable_lemmatizer_not_annotated():
 def test_project_api_imports():
     from spacy.cli import project_run
     from spacy.cli.project.run import project_run  # noqa: F401, F811
+
+
+def test_download_rejects_relative_urls(monkeypatch):
+    """Test that we can't tell spacy download to get an arbitrary model by using a
+    relative path in the filename"""
+
+    monkeypatch.setattr(download_module, "run_command", lambda cmd: None)
+
+    # Check that normal download works
+    download_module.download("en_core_web_sm-3.7.1", direct=True)
+    with pytest.raises(SystemExit):
+        download_module.download("../en_core_web_sm-3.7.1", direct=True)

spacy/tests/test_language.py

@@ -1,5 +1,6 @@
 import itertools
 import logging
+import warnings
 from unittest import mock
 
 import pytest
@@ -423,7 +424,7 @@ def test_language_pipe_error_handler(n_process):
         nlp.set_error_handler(raise_error)
         with pytest.raises(ValueError):
             list(nlp.pipe(texts, n_process=n_process))
-        # set explicitely to ignoring
+        # set explicitly to ignoring
         nlp.set_error_handler(ignore_error)
         docs = list(nlp.pipe(texts, n_process=n_process))
         assert len(docs) == 0
@@ -834,6 +835,10 @@ def test_pass_doc_to_pipeline(nlp, n_process):
     assert doc.text == texts[0]
     assert len(doc.cats) > 0
     if isinstance(get_current_ops(), NumpyOps) or n_process < 2:
+        # Catch warnings to ensure that all worker processes exited
+        # succesfully.
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
             docs = nlp.pipe(docs, n_process=n_process)
             assert [doc.text for doc in docs] == texts
             assert all(len(doc.cats) for doc in docs)

spacy/tests/tok2vec.py

@@ -0,0 +1,36 @@
+from typing import List
+
+from thinc.api import Model
+from thinc.types import Floats2d
+
+from spacy.tokens import Doc
+from spacy.util import registry
+
+
+@registry.architectures("test.LazyInitTok2Vec.v1")
+def build_lazy_init_tok2vec(*, width: int) -> Model[List[Doc], List[Floats2d]]:
+    """tok2vec model of which the output size is only known after
+    initialization. This implementation does not output meaningful
+    embeddings, it is strictly for testing."""
+    return Model(
+        "lazy_init_tok2vec",
+        lazy_init_tok2vec_forward,
+        init=lazy_init_tok2vec_init,
+        dims={"nO": None},
+        attrs={"width": width},
+    )
+
+
+def lazy_init_tok2vec_init(model: Model, X=None, Y=None):
+    width = model.attrs["width"]
+    model.set_dim("nO", width)
+
+
+def lazy_init_tok2vec_forward(model: Model, X: List[Doc], is_train: bool):
+    width = model.get_dim("nO")
+    Y = [model.ops.alloc2f(len(doc), width) for doc in X]
+
+    def backprop(dY):
+        return []
+
+    return Y, backprop

spacy/tokenizer.pyx

@@ -71,51 +71,57 @@ cdef class Tokenizer:
         self._special_matcher = PhraseMatcher(self.vocab)
         self._load_special_cases(rules)
 
-    property token_match:
+    @property
-        def __get__(self):
+    def token_match(self):
         return self._token_match
 
-        def __set__(self, token_match):
+    @token_match.setter
+    def token_match(self, token_match):
         self._token_match = token_match
         self._reload_special_cases()
 
-    property url_match:
+    @property
-        def __get__(self):
+    def url_match(self):
         return self._url_match
 
-        def __set__(self, url_match):
+    @url_match.setter
+    def url_match(self, url_match):
         self._url_match = url_match
         self._reload_special_cases()
 
-    property prefix_search:
+    @property
-        def __get__(self):
+    def prefix_search(self):
         return self._prefix_search
 
-        def __set__(self, prefix_search):
+    @prefix_search.setter
+    def prefix_search(self, prefix_search):
         self._prefix_search = prefix_search
         self._reload_special_cases()
 
-    property suffix_search:
+    @property
-        def __get__(self):
+    def suffix_search(self):
         return self._suffix_search
 
-        def __set__(self, suffix_search):
+    @suffix_search.setter
+    def suffix_search(self, suffix_search):
         self._suffix_search = suffix_search
         self._reload_special_cases()
 
-    property infix_finditer:
+    @property
-        def __get__(self):
+    def infix_finditer(self):
         return self._infix_finditer
 
-        def __set__(self, infix_finditer):
+    @infix_finditer.setter
+    def infix_finditer(self, infix_finditer):
         self._infix_finditer = infix_finditer
         self._reload_special_cases()
 
-    property rules:
+    @property
-        def __get__(self):
+    def rules(self):
         return self._rules
 
-        def __set__(self, rules):
+    @rules.setter
+    def rules(self, rules):
         self._rules = {}
         self._flush_cache()
         self._flush_specials()
@@ -123,11 +129,12 @@ cdef class Tokenizer:
         self._specials = PreshMap()
         self._load_special_cases(rules)
 
-    property faster_heuristics:
+    @property
-        def __get__(self):
+    def faster_heuristics(self):
         return self._faster_heuristics
 
-        def __set__(self, faster_heuristics):
+    @faster_heuristics.setter
+    def faster_heuristics(self, faster_heuristics):
         self._faster_heuristics = faster_heuristics
         self._reload_special_cases()
 

spacy/tokens/doc.pyx

@@ -667,7 +667,8 @@ cdef class Doc:
         else:
             return False
 
-    property vector:
+    @property
+    def vector(self):
         """A real-valued meaning representation. Defaults to an average of the
         token vectors.
 
@@ -676,7 +677,6 @@ cdef class Doc:
 
         DOCS: https://spacy.io/api/doc#vector
         """
-        def __get__(self):
         if "vector" in self.user_hooks:
             return self.user_hooks["vector"](self)
         if self._vector is not None:
@@ -691,17 +691,18 @@ cdef class Doc:
         else:
             return xp.zeros((self.vocab.vectors_length,), dtype="float32")
 
-        def __set__(self, value):
+    @vector.setter
+    def vector(self, value):
         self._vector = value
 
-    property vector_norm:
+    @property
+    def vector_norm(self):
         """The L2 norm of the document's vector representation.
 
         RETURNS (float): The L2 norm of the vector representation.
 
         DOCS: https://spacy.io/api/doc#vector_norm
         """
-        def __get__(self):
         if "vector_norm" in self.user_hooks:
             return self.user_hooks["vector_norm"](self)
         cdef float value
@@ -713,7 +714,8 @@ cdef class Doc:
             self._vector_norm = sqrt(norm) if norm != 0 else 0
         return self._vector_norm
 
-        def __set__(self, value):
+    @vector_norm.setter
+    def vector_norm(self, value):
         self._vector_norm = value
 
     @property
@@ -733,7 +735,8 @@ cdef class Doc:
         """
         return self.text
 
-    property ents:
+    @property
+    def ents(self):
         """The named entities in the document. Returns a list of named entity
         `Span` objects, if the entity recognizer has been applied.
 
@@ -741,7 +744,6 @@ cdef class Doc:
 
         DOCS: https://spacy.io/api/doc#ents
         """
-        def __get__(self):
         cdef int i
         cdef const TokenC* token
         cdef int start = -1
@@ -776,7 +778,8 @@ cdef class Doc:
         output = [o for o in output if o.label_ != ""]
         return tuple(output)
 
-        def __set__(self, ents):
+    @ents.setter
+    def ents(self, ents):
         # TODO:
         # 1. Test basic data-driven ORTH gazetteer
         # 2. Test more nuanced date and currency regex

spacy/tokens/span.pyx

@@ -786,109 +786,129 @@ cdef class Span:
         for word in self.rights:
             yield from word.subtree
 
-    property start:
+    @property
-        def __get__(self):
+    def start(self):
         return self.span_c().start
 
-        def __set__(self, int start):
+    @start.setter
+    def start(self, int start):
         if start < 0 or start > self.doc.length:
             raise IndexError(Errors.E1032.format(var="start", obj="Doc", length=self.doc.length, value=start))
-            cdef SpanC* span_c = self.span_c()
+        cdef SpanC * span_c = self.span_c()
         if start > span_c.end:
-                raise ValueError(Errors.E4007.format(var="start", value=start, op="<=", existing_var="end", existing_value=span_c.end))
+            raise ValueError(
+                Errors.E4007.format(var="start", value=start, op="<=", existing_var="end", existing_value=span_c.end))
         span_c.start = start
         span_c.start_char = self.doc.c[start].idx
 
-    property end:
+    @property
-        def __get__(self):
+    def end(self):
         return self.span_c().end
 
-        def __set__(self, int end):
+    @end.setter
+    def end(self, int end):
         if end < 0 or end > self.doc.length:
             raise IndexError(Errors.E1032.format(var="end", obj="Doc", length=self.doc.length, value=end))
-            cdef SpanC* span_c = self.span_c()
+        cdef SpanC * span_c = self.span_c()
         if span_c.start > end:
-                raise ValueError(Errors.E4007.format(var="end", value=end, op=">=", existing_var="start", existing_value=span_c.start))
+            raise ValueError(
+                Errors.E4007.format(var="end", value=end, op=">=", existing_var="start", existing_value=span_c.start))
         span_c.end = end
         if end > 0:
-                span_c.end_char = self.doc.c[end-1].idx + self.doc.c[end-1].lex.length
+            span_c.end_char = self.doc.c[end - 1].idx + self.doc.c[end - 1].lex.length
         else:
             span_c.end_char = 0
 
-    property start_char:
+    @property
-        def __get__(self):
+    def start_char(self):
         return self.span_c().start_char
 
-        def __set__(self, int start_char):
+    @start_char.setter
+    def start_char(self, int start_char):
         if start_char < 0 or start_char > len(self.doc.text):
-                raise IndexError(Errors.E1032.format(var="start_char", obj="Doc text", length=len(self.doc.text), value=start_char))
+            raise IndexError(
+                Errors.E1032.format(var="start_char", obj="Doc text", length=len(self.doc.text), value=start_char))
         cdef int start = token_by_start(self.doc.c, self.doc.length, start_char)
         if start < 0:
             raise ValueError(Errors.E4008.format(value=start_char, pos="start"))
-            cdef SpanC* span_c = self.span_c()
+        cdef SpanC * span_c = self.span_c()
         if start_char > span_c.end_char:
-                raise ValueError(Errors.E4007.format(var="start_char", value=start_char, op="<=", existing_var="end_char", existing_value=span_c.end_char))
+            raise ValueError(Errors.E4007.format(var="start_char", value=start_char, op="<=", existing_var="end_char",
+                                                 existing_value=span_c.end_char))
         span_c.start_char = start_char
         span_c.start = start
 
-    property end_char:
+    @property
-        def __get__(self):
+    def end_char(self):
         return self.span_c().end_char
 
-        def __set__(self, int end_char):
+    @end_char.setter
+    def end_char(self, int end_char):
         if end_char < 0 or end_char > len(self.doc.text):
-                raise IndexError(Errors.E1032.format(var="end_char", obj="Doc text", length=len(self.doc.text), value=end_char))
+            raise IndexError(
+                Errors.E1032.format(var="end_char", obj="Doc text", length=len(self.doc.text), value=end_char))
         cdef int end = token_by_end(self.doc.c, self.doc.length, end_char)
         if end < 0:
             raise ValueError(Errors.E4008.format(value=end_char, pos="end"))
-            cdef SpanC* span_c = self.span_c()
+        cdef SpanC * span_c = self.span_c()
         if span_c.start_char > end_char:
-                raise ValueError(Errors.E4007.format(var="end_char", value=end_char, op=">=", existing_var="start_char", existing_value=span_c.start_char))
+            raise ValueError(Errors.E4007.format(var="end_char", value=end_char, op=">=", existing_var="start_char",
+                                                 existing_value=span_c.start_char))
         span_c.end_char = end_char
         span_c.end = end
 
-    property label:
+    @property
-        def __get__(self):
+    def label(self):
         return self.span_c().label
 
-        def __set__(self, attr_t label):
+    @label.setter
-            if label != self.span_c().label :
+    def label(self, attr_t label):
+        if label != self.span_c().label:
             old_label = self.span_c().label
             self.span_c().label = label
-                new = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char, end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
+            new = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char,
-                old = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char, end=self.span_c().end_char, label=old_label, kb_id=self.kb_id, span_id=self.id)
+                             end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
+            old = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char,
+                             end=self.span_c().end_char, label=old_label, kb_id=self.kb_id, span_id=self.id)
             Underscore._replace_keys(old, new)
 
-    property kb_id:
+    @property
-        def __get__(self):
+    def kb_id(self):
         return self.span_c().kb_id
 
-        def __set__(self, attr_t kb_id):
+    @kb_id.setter
-            if kb_id != self.span_c().kb_id :
+    def kb_id(self, attr_t kb_id):
+        if kb_id != self.span_c().kb_id:
             old_kb_id = self.span_c().kb_id
             self.span_c().kb_id = kb_id
-                new = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char, end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
+            new = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char,
-                old = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char, end=self.span_c().end_char, label=self.label, kb_id=old_kb_id, span_id=self.id)
+                             end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
+            old = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char,
+                             end=self.span_c().end_char, label=self.label, kb_id=old_kb_id, span_id=self.id)
             Underscore._replace_keys(old, new)
 
-    property id:
+    @property
-        def __get__(self):
+    def id(self):
         return self.span_c().id
 
-        def __set__(self, attr_t id):
+    @id.setter
-            if id != self.span_c().id :
+    def id(self, attr_t id):
+        if id != self.span_c().id:
             old_id = self.span_c().id
             self.span_c().id = id
-                new = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char, end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
+            new = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char,
-                old = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char, end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=old_id)
+                             end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=self.id)
+            old = Underscore(Underscore.span_extensions, self, start=self.span_c().start_char,
+                             end=self.span_c().end_char, label=self.label, kb_id=self.kb_id, span_id=old_id)
             Underscore._replace_keys(old, new)
 
-    property ent_id:
+    @property
+    def ent_id(self):
         """Alias for the span's ID."""
-        def __get__(self):
         return self.id
 
-        def __set__(self, attr_t ent_id):
+    @ent_id.setter
+    def ent_id(self, attr_t ent_id):
         self.id = ent_id
 
     @property
@@ -904,28 +924,31 @@ cdef class Span:
         """RETURNS (str): The span's lemma."""
         return "".join([t.lemma_ + t.whitespace_ for t in self]).strip()
 
-    property label_:
+    @property
+    def label_(self):
         """The span's label."""
-        def __get__(self):
         return self.doc.vocab.strings[self.label]
 
-        def __set__(self, str label_):
+    @label_.setter
+    def label_(self, str label_):
         self.label = self.doc.vocab.strings.add(label_)
 
-    property kb_id_:
+    @property
+    def kb_id_(self):
         """The span's KB ID."""
-        def __get__(self):
         return self.doc.vocab.strings[self.kb_id]
 
-        def __set__(self, str kb_id_):
+    @kb_id_.setter
+    def kb_id_(self, str kb_id_):
         self.kb_id = self.doc.vocab.strings.add(kb_id_)
 
-    property id_:
+    @property
+    def id_(self):
         """The span's ID."""
-        def __get__(self):
         return self.doc.vocab.strings[self.id]
 
-        def __set__(self, str id_):
+    @id_.setter
+    def id_(self, str id_):
         self.id = self.doc.vocab.strings.add(id_)
 
     property ent_id_:

spacy/tokens/token.pyx

@@ -250,11 +250,12 @@ cdef class Token:
         """
         return not self.c.morph == 0
 
-    property morph:
+    @property
-        def __get__(self):
+    def morph(self):
         return MorphAnalysis.from_id(self.vocab, self.c.morph)
 
-        def __set__(self, MorphAnalysis morph):
+    @morph.setter
+    def morph(self, MorphAnalysis morph):
         # Check that the morph has the same vocab
         if self.vocab != morph.vocab:
             raise ValueError(Errors.E1013)
@@ -370,38 +371,42 @@ cdef class Token:
         """
         return self.c.lex.suffix
 
-    property lemma:
+    @property
+    def lemma(self):
         """RETURNS (uint64): ID of the base form of the word, with no
             inflectional suffixes.
         """
-        def __get__(self):
         return self.c.lemma
 
-        def __set__(self, attr_t lemma):
+    @lemma.setter
+    def lemma(self, attr_t lemma):
         self.c.lemma = lemma
 
-    property pos:
+    @property
+    def pos(self):
         """RETURNS (uint64): ID of coarse-grained part-of-speech tag."""
-        def __get__(self):
         return self.c.pos
 
-        def __set__(self, pos):
+    @pos.setter
+    def pos(self, pos):
         self.c.pos = pos
 
-    property tag:
+    @property
+    def tag(self):
         """RETURNS (uint64): ID of fine-grained part-of-speech tag."""
-        def __get__(self):
         return self.c.tag
 
-        def __set__(self, attr_t tag):
+    @tag.setter
+    def tag(self, attr_t tag):
         self.c.tag = tag
 
-    property dep:
+    @property
+    def dep(self):
         """RETURNS (uint64): ID of syntactic dependency label."""
-        def __get__(self):
         return self.c.dep
 
-        def __set__(self, attr_t label):
+    @dep.setter
+    def dep(self, attr_t label):
         self.c.dep = label
 
     @property
@@ -483,8 +488,8 @@ cdef class Token:
             return self.doc.user_token_hooks["sent"](self)
         return self.doc[self.i : self.i+1].sent
 
-    property sent_start:
+    @property
-        def __get__(self):
+    def sent_start(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
@@ -494,17 +499,18 @@ cdef class Token:
         else:
             return self.c.sent_start
 
-        def __set__(self, value):
+    @sent_start.setter
+    def sent_start(self, value):
         self.is_sent_start = value
 
-    property is_sent_start:
+    @property
+    def is_sent_start(self):
         """A boolean value indicating whether the token starts a sentence.
         `None` if unknown. Defaults to `True` for the first token in the `Doc`.
 
         RETURNS (bool / None): Whether the token starts a sentence.
             None if unknown.
         """
-        def __get__(self):
         if self.c.sent_start == 0:
             return None
         elif self.c.sent_start < 0:
@@ -512,7 +518,8 @@ cdef class Token:
         else:
             return True
 
-        def __set__(self, value):
+    @is_sent_start.setter
+    def is_sent_start(self, value):
         if self.doc.has_annotation("DEP"):
             raise ValueError(Errors.E043)
         if value is None:
@@ -524,7 +531,8 @@ cdef class Token:
         else:
             raise ValueError(Errors.E044.format(value=value))
 
-    property is_sent_end:
+    @property
+    def is_sent_end(self):
         """A boolean value indicating whether the token ends a sentence.
         `None` if unknown. Defaults to `True` for the last token in the `Doc`.
 
@@ -533,7 +541,6 @@ cdef class Token:
 
         DOCS: https://spacy.io/api/token#is_sent_end
         """
-        def __get__(self):
         if self.i + 1 == len(self.doc):
             return True
         elif self.doc[self.i+1].is_sent_start is None:
@@ -543,7 +550,8 @@ cdef class Token:
         else:
             return False
 
-        def __set__(self, value):
+    @is_sent_end.setter
+    def is_sent_end(self, value):
         raise ValueError(Errors.E196)
 
     @property
@@ -671,20 +679,21 @@ cdef class Token:
         """
         return not Token.missing_head(self.c)
 
-    property head:
+    @property
+    def head(self):
         """The syntactic parent, or "governor", of this token.
         If token.has_head() is `False`, this method will return itself.
 
         RETURNS (Token): The token predicted by the parser to be the head of
             the current token.
         """
-        def __get__(self):
         if not self.has_head():
             return self
         else:
             return self.doc[self.i + self.c.head]
 
-        def __set__(self, Token new_head):
+    @head.setter
+    def head(self, Token new_head):
         # This function sets the head of self to new_head and updates the
         # counters for left/right dependents and left/right corner for the
         # new and the old head
@@ -733,20 +742,22 @@ cdef class Token:
                     queue.append(child)
         return tuple([w for w in output if w.i != self.i])
 
-    property ent_type:
+    @property
+    def ent_type(self):
         """RETURNS (uint64): Named entity type."""
-        def __get__(self):
         return self.c.ent_type
 
-        def __set__(self, ent_type):
+    @ent_type.setter
+    def ent_type(self, ent_type):
         self.c.ent_type = ent_type
 
-    property ent_type_:
+    @property
+    def ent_type_(self):
         """RETURNS (str): Named entity type."""
-        def __get__(self):
         return self.vocab.strings[self.c.ent_type]
 
-        def __set__(self, ent_type):
+    @ent_type_.setter
+    def ent_type_(self, ent_type):
         self.c.ent_type = self.vocab.strings.add(ent_type)
 
     @property
@@ -773,40 +784,44 @@ cdef class Token:
         """
         return self.iob_strings()[self.c.ent_iob]
 
-    property ent_id:
+    @property
+    def ent_id(self):
         """RETURNS (uint64): ID of the entity the token is an instance of,
             if any.
         """
-        def __get__(self):
         return self.c.ent_id
 
-        def __set__(self, hash_t key):
+    @ent_id.setter
+    def ent_id(self, hash_t key):
         self.c.ent_id = key
 
-    property ent_id_:
+    @property
+    def ent_id_(self):
         """RETURNS (str): ID of the entity the token is an instance of,
             if any.
         """
-        def __get__(self):
         return self.vocab.strings[self.c.ent_id]
 
-        def __set__(self, name):
+    @ent_id_.setter
+    def ent_id_(self, name):
         self.c.ent_id = self.vocab.strings.add(name)
 
-    property ent_kb_id:
+    @property
+    def ent_kb_id(self):
         """RETURNS (uint64): Named entity KB ID."""
-        def __get__(self):
         return self.c.ent_kb_id
 
-        def __set__(self, attr_t ent_kb_id):
+    @ent_kb_id.setter
+    def ent_kb_id(self, attr_t ent_kb_id):
         self.c.ent_kb_id = ent_kb_id
 
-    property ent_kb_id_:
+    @property
+    def ent_kb_id_(self):
         """RETURNS (str): Named entity KB ID."""
-        def __get__(self):
         return self.vocab.strings[self.c.ent_kb_id]
 
-        def __set__(self, ent_kb_id):
+    @ent_kb_id_.setter
+    def ent_kb_id_(self, ent_kb_id):
         self.c.ent_kb_id = self.vocab.strings.add(ent_kb_id)
 
     @property
@@ -829,15 +844,16 @@ cdef class Token:
         """
         return self.vocab.strings[self.c.lex.lower]
 
-    property norm_:
+    @property
+    def norm_(self):
         """RETURNS (str): The token's norm, i.e. a normalised form of the
             token text. Usually set in the language's tokenizer exceptions or
             norm exceptions.
         """
-        def __get__(self):
         return self.vocab.strings[self.norm]
 
-        def __set__(self, str norm_):
+    @norm_.setter
+    def norm_(self, str norm_):
         self.c.norm = self.vocab.strings.add(norm_)
 
     @property
@@ -868,32 +884,35 @@ cdef class Token:
         """
         return self.vocab.strings[self.c.lex.lang]
 
-    property lemma_:
+    @property
+    def lemma_(self):
         """RETURNS (str): The token lemma, i.e. the base form of the word,
             with no inflectional suffixes.
         """
-        def __get__(self):
         return self.vocab.strings[self.c.lemma]
 
-        def __set__(self, str lemma_):
+    @lemma_.setter
+    def lemma_(self, str lemma_):
         self.c.lemma = self.vocab.strings.add(lemma_)
 
-    property pos_:
+    @property
+    def pos_(self):
         """RETURNS (str): Coarse-grained part-of-speech tag."""
-        def __get__(self):
         return parts_of_speech.NAMES[self.c.pos]
 
-        def __set__(self, pos_name):
+    @pos_.setter
+    def pos_(self, pos_name):
         if pos_name not in parts_of_speech.IDS:
             raise ValueError(Errors.E1021.format(pp=pos_name))
         self.c.pos = parts_of_speech.IDS[pos_name]
 
-    property tag_:
+    @property
+    def tag_(self):
         """RETURNS (str): Fine-grained part-of-speech tag."""
-        def __get__(self):
         return self.vocab.strings[self.c.tag]
 
-        def __set__(self, tag):
+    @tag_.setter
+    def tag_(self, tag):
         self.tag = self.vocab.strings.add(tag)
 
     def has_dep(self):
@@ -904,12 +923,13 @@ cdef class Token:
         """
         return not Token.missing_dep(self.c)
 
-    property dep_:
+    @property
+    def dep_(self):
         """RETURNS (str): The syntactic dependency label."""
-        def __get__(self):
         return self.vocab.strings[self.c.dep]
 
-        def __set__(self, str label):
+    @dep_.setter
+    def dep_(self, str label):
         self.c.dep = self.vocab.strings.add(label)
 
     @property

spacy/training/example.pyx

@@ -101,20 +101,22 @@ cdef class Example:
     def __len__(self):
         return len(self.predicted)
 
-    property predicted:
+    @property
-        def __get__(self):
+    def predicted(self):
         return self.x
 
-        def __set__(self, doc):
+    @predicted.setter
+    def predicted(self, doc):
         self.x = doc
         self._cached_alignment = None
         self._cached_words_x = [t.text for t in doc]
 
-    property reference:
+    @property
-        def __get__(self):
+    def reference(self):
         return self.y
 
-        def __set__(self, doc):
+    @reference.setter
+    def reference(self, doc):
         self.y = doc
         self._cached_alignment = None
         self._cached_words_y = [t.text for t in doc]
@@ -433,8 +435,8 @@ cdef class Example:
                 seen_indices.update(indices)
         return output
 
-    property text:
+    @property
-        def __get__(self):
+    def text(self):
         return self.x.text
 
     def __str__(self):

spacy/vocab.pyx

@@ -87,11 +87,12 @@ cdef class Vocab:
             self.writing_system = writing_system
         self.get_noun_chunks = get_noun_chunks
 
-    property vectors:
+    @property
-        def __get__(self):
+    def vectors(self):
         return self._vectors
 
-        def __set__(self, vectors):
+    @vectors.setter
+    def vectors(self, vectors):
         if hasattr(vectors, "strings"):
             for s in vectors.strings:
                 self.strings.add(s)
@@ -450,11 +451,12 @@ cdef class Vocab:
         key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
         return key in self.vectors
 
-    property lookups:
+    @property
-        def __get__(self):
+    def lookups(self):
         return self._lookups
 
-        def __set__(self, lookups):
+    @lookups.setter
+    def lookups(self, lookups):
         self._lookups = lookups
         if lookups.has_table("lexeme_norm"):
             self.lex_attr_getters[NORM] = util.add_lookups(

website/docs/api/attributes.mdx

@@ -46,10 +46,10 @@ as `Token.attr`, e.g. `token.dep`, while the string value can be retrieved by
 appending `_` as in `token.dep_`.
 
 | Attribute    | Description                                                                                                                                                    |
-| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `DEP`        | The token's dependency label. ~~str~~                                                                                                                          |
 | `ENT_ID`     | The token's entity ID (`ent_id`). ~~str~~                                                                                                                      |
-| `ENT_IOB`    | The IOB part of the token's entity tag. Uses custom integer vaues rather than the string store: unset is `0`, `I` is `1`, `O` is `2`, and `B` is `3`. ~~str~~ |
+| `ENT_IOB`    | The IOB part of the token's entity tag. Uses custom integer values rather than the string store: unset is `0`, `I` is `1`, `O` is `2`, and `B` is `3`. ~~str~~ |
 | `ENT_KB_ID`  | The token's entity knowledge base ID. ~~str~~                                                                                                                  |
 | `ENT_TYPE`   | The token's entity label. ~~str~~                                                                                                                              |
 | `IS_ALPHA`   | Token text consists of alphabetic characters. ~~bool~~                                                                                                         |

website/docs/api/cli.mdx

@@ -566,7 +566,7 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
 'LOC' (2113), 'TIME' (1616), 'WORK_OF_ART' (1229), 'QUANTITY' (1150), 'FAC'
 (1134), 'EVENT' (974), 'PRODUCT' (935), 'LAW' (444), 'LANGUAGE' (338)
 ✔ Good amount of examples for all labels
-✔ Examples without occurences available for all labels
+✔ Examples without occurrences available for all labels
 ✔ No entities consisting of or starting/ending with whitespace
 
 =========================== Part-of-speech Tagging ===========================
@@ -1322,7 +1322,7 @@ $ python -m spacy apply [model] [data-path] [output-file] [--code] [--text-key]
 
 ## find-threshold {id="find-threshold",version="3.5",tag="command"}
 
-Runs prediction trials for a trained model with varying tresholds to maximize
+Runs prediction trials for a trained model with varying thresholds to maximize
 the specified metric. The search space for the threshold is traversed linearly
 from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
 (the corresponding API call to `spacy.cli.find_threshold.find_threshold()`

website/docs/api/entitylinker.mdx

@@ -61,13 +61,13 @@ architectures and their arguments and hyperparameters.
 | `incl_context`                                   | Whether the local context is included in the model. Defaults to `True`. ~~bool~~                                                                                                                                                                                                                                                               |
 | `model`                                          | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [`EntityLinker`](/api/architectures#EntityLinker). ~~Model~~                                                                                                                                                                                       |
 | `entity_vector_length`                           | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~                                                                                                                                                                                                                                                                                  |
-| `use_gold_ents`                                  | Whether entities are copied from the gold docs. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~int~~                                                                                                                                                               |
+| `use_gold_ents`                                  | Whether entities are copied from the gold docs. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~bool~~                                                                                                                                                              |
 | `get_candidates` <Tag variant="new">4.0</Tag>    | Function that retrieves plausible candidates per entity mention in a given `Iterator[SpanGroup]` (one `SpanGroup` includes all mentions found in a given `Doc` instance). Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator). ~~Callable[[KnowledgeBase, Iterator[SpanGroup]], Iterator[Iterable[Iterable[Candidate]]]]~~ |
 | `generate_empty_kb` <Tag variant="new">3.6</Tag> | Function that generates an empty `KnowledgeBase` object. Defaults to [`spacy.EmptyKB.v2`](/api/architectures#EmptyKB), which generates an empty [`InMemoryLookupKB`](/api/inmemorylookupkb). ~~Callable[[Vocab, int], KnowledgeBase]~~                                                                                                         |
 | `overwrite` <Tag variant="new">3.2</Tag>         | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~                                                                                                                                                                                                                                                                       |
 | `scorer` <Tag variant="new">3.2</Tag>            | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~                                                                                                                                                                                                                                        |
 | `save_activations` <Tag variant="new">4.0</Tag>  | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~                                                                                                                                                                                                                           |
-| `threshold` <Tag variant="new">3.4</Tag>         | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~                                                    |
+| `threshold` <Tag variant="new">3.4</Tag>         | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the threshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~                                                   |
 
 <Infobox variant="warning">
 
@@ -115,7 +115,7 @@ custom knowledge base, you should either call
 [`initialize`](/api/entitylinker#initialize) call.
 
 | Name                                     | Description                                                                                                                                                                                                                                                                                  |
-| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ---------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `vocab`                                  | The shared vocabulary. ~~Vocab~~                                                                                                                                                                                                                                                             |
 | `model`                                  | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~                                                                                                                                                                                                    |
 | `name`                                   | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                                                                                                                                                                                          |
@@ -128,7 +128,7 @@ custom knowledge base, you should either call
 | `incl_context`                           | Whether or not to include the local context in the model. ~~bool~~                                                                                                                                                                                                                           |
 | `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~                                                                                                                                                                                                                     |
 | `scorer` <Tag variant="new">3.2</Tag>    | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~                                                                                                                                                                                      |
-| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
+| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the threshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
 
 ## EntityLinker.\_\_call\_\_ {id="call",tag="method"}
 

website/docs/api/morphology.mdx

@@ -148,8 +148,9 @@ Whether a feature/value pair is in the analysis.
 > ```
 
 | Name         | Description                                                           |
-| ----------- | --------------------------------------------- |
+| ------------ | --------------------------------------------------------------------- |
-| **RETURNS** | A feature/value pair in the analysis. ~~str~~ |
+| `feature`    | A feature/value pair. ~~str~~                                         |
+| **RETURNS**  | Whether the feature/value pair is contained in the analysis. ~~bool~~ |
 
 ### MorphAnalysis.\_\_iter\_\_ {id="morphanalysis-iter",tag="method"}
 

website/docs/api/span.mdx

@@ -287,7 +287,7 @@ does not permit other NPs to be nested within it – so no NP-level coordination
 no prepositional phrases, and no relative clauses.
 
 If the `noun_chunk` [syntax iterator](/usage/linguistic-features#language-data)
-has not been implemeted for the given language, a `NotImplementedError` is
+has not been implemented for the given language, a `NotImplementedError` is
 raised.
 
 > #### Example

website/docs/api/transformer.mdx

@@ -416,7 +416,7 @@ by this class. Instances of this class are typically assigned to the
 | `align`        | Alignment from the `Doc`'s tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~                                                                                 |
 | `width`        | The width of the last hidden layer. ~~int~~                                                                                                                                                                                                                                                                                          |
 
-### TransformerData.empty {id="transformerdata-emoty",tag="classmethod"}
+### TransformerData.empty {id="transformerdata-empty",tag="classmethod"}
 
 Create an empty `TransformerData` container.
 

website/docs/usage/layers-architectures.mdx

@@ -832,7 +832,7 @@ retrieve and add to them.
 
 After creation, the component needs to be
 [initialized](/usage/training#initialization). This method can define the
-relevant labels in two ways: explicitely by setting the `labels` argument in the
+relevant labels in two ways: explicitly by setting the `labels` argument in the
 [`initialize` block](/api/data-formats#config-initialize) of the config, or
 implicately by deducing them from the `get_examples` callback that generates the
 full **training data set**, or a representative sample.

website/docs/usage/linguistic-features.mdx

@@ -1899,7 +1899,7 @@ the two words.
     "Shore": ("coast", 0.732257),
     "Precautionary": ("caution", 0.490973),
     "hopelessness": ("sadness", 0.742366),
-    "Continous": ("continuous", 0.732549),
+    "Continuous": ("continuous", 0.732549),
     "Disemboweled": ("corpse", 0.499432),
     "biostatistician": ("scientist", 0.339724),
     "somewheres": ("somewheres", 0.402736),

website/docs/usage/models.mdx

@@ -530,13 +530,17 @@ application's `requirements.txt`. If you're running your own internal PyPi
 installation, you can upload the pipeline packages there. pip's
 [requirements file format](https://pip.pypa.io/en/latest/reference/requirements-file-format/)
 supports both package names to download via a PyPi server, as well as
-[direct URLs](#pipeline-urls).
+[direct URLs](#pipeline-urls). For instance, you can specify the
+`en_core_web_sm` model for spaCy 3.7.x as follows:
 
 ```text {title="requirements.txt"}
 spacy>=3.0.0,<4.0.0
-en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.0/en_core_web_sm-3.4.0-py3-none-any.whl
+en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl
 ```
 
+See the [list of models](https://spacy.io/models) for model download links for
+the current spaCy version.
+
 All pipeline packages are versioned and specify their spaCy dependency. This
 ensures cross-compatibility and lets you specify exact version requirements for
 each pipeline. If you've [trained](/usage/training) your own pipeline, you can

website/docs/usage/projects.mdx

@@ -173,7 +173,7 @@ detected, a corresponding warning is displayed. If you'd like to disable the
 dependency check, set `check_requirements: false` in your project's
 `project.yml`.
 
-### 4. Run a workflow {id="run-workfow"}
+### 4. Run a workflow {id="run-workflow"}
 
 > #### project.yml
 >
@@ -286,7 +286,7 @@ pipelines.
 | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `title`                                             | An optional project title used in `--help` message and [auto-generated docs](#custom-docs).                                                                                                                                                                                                                                                                                                                                                                                                                  |
 | `description`                                       | An optional project description used in [auto-generated docs](#custom-docs).                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| `vars`                                              | A dictionary of variables that can be referenced in paths, URLs and scripts and overriden on the CLI, just like [`config.cfg` variables](/usage/training#config-interpolation). For example, `${vars.name}` will use the value of the variable `name`. Variables need to be defined in the section `vars`, but can be a nested dict, so you're able to reference `${vars.model.name}`.                                                                                                                       |
+| `vars`                                              | A dictionary of variables that can be referenced in paths, URLs and scripts and overridden on the CLI, just like [`config.cfg` variables](/usage/training#config-interpolation). For example, `${vars.name}` will use the value of the variable `name`. Variables need to be defined in the section `vars`, but can be a nested dict, so you're able to reference `${vars.model.name}`.                                                                                                                      |
 | `env`                                               | A dictionary of variables, mapped to the names of environment variables that will be read in when running the project. For example, `${env.name}` will use the value of the environment variable defined as `name`.                                                                                                                                                                                                                                                                                          |
 | `directories`                                       | An optional list of [directories](#project-files) that should be created in the project for assets, training outputs, metrics etc. spaCy will make sure that these directories always exist.                                                                                                                                                                                                                                                                                                                 |
 | `assets`                                            | A list of assets that can be fetched with the [`project assets`](/api/cli#project-assets) command. `url` defines a URL or local path, `dest` is the destination file relative to the project directory, and an optional `checksum` ensures that an error is raised if the file's checksum doesn't match. Instead of `url`, you can also provide a `git` block with the keys `repo`, `branch` and `path`, to download from a Git repo.                                                                        |

website/docs/usage/saving-loading.mdx

@@ -306,7 +306,9 @@ installed in the same environment – that's it.
 
 ### Loading probability tables into existing models
 
-You can load a probability table from [spacy-lookups-data](https://github.com/explosion/spacy-lookups-data) into an existing spaCy model like `en_core_web_sm`.
+You can load a probability table from
+[spacy-lookups-data](https://github.com/explosion/spacy-lookups-data) into an
+existing spaCy model like `en_core_web_sm`.
 
 ```python
 # Requirements: pip install spacy-lookups-data
@@ -317,7 +319,8 @@ lookups = load_lookups("en", ["lexeme_prob"])
 nlp.vocab.lookups.add_table("lexeme_prob", lookups.get_table("lexeme_prob"))
 ```
 
-When training a model from scratch you can also specify probability tables in the `config.cfg`.
+When training a model from scratch you can also specify probability tables in
+the `config.cfg`.
 
 ```ini {title="config.cfg (excerpt)"}
 [initialize.lookups]
@@ -346,8 +349,8 @@ them**!
 To stick with the theme of
 [this entry points blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
 consider the following custom spaCy
-[pipeline component](/usage/processing-pipelines#custom-coponents) that prints a
+[pipeline component](/usage/processing-pipelines#custom-components) that prints
-snake when it's called:
+a snake when it's called:
 
 > #### Package directory structure
 >

website/docs/usage/v2-2.mdx

@@ -185,7 +185,7 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
 'LOC' (2113), 'TIME' (1616), 'WORK_OF_ART' (1229), 'QUANTITY' (1150), 'FAC'
 (1134), 'EVENT' (974), 'PRODUCT' (935), 'LAW' (444), 'LANGUAGE' (338)
 ✔ Good amount of examples for all labels
-✔ Examples without occurences available for all labels
+✔ Examples without occurrences available for all labels
 ✔ No entities consisting of or starting/ending with whitespace
 
 =========================== Part-of-speech Tagging ===========================

website/docs/usage/v3-2.mdx

@@ -138,7 +138,7 @@ backwards compatibility, the tuple format remains available under
 `TransformerData.tensors` and `FullTransformerBatch.tensors`. See more details
 in the [transformer API docs](/api/architectures#TransformerModel).
 
-`spacy-transfomers` v1.1 also adds support for `transformer_config` settings
+`spacy-transformers` v1.1 also adds support for `transformer_config` settings
 such as `output_attentions`. Additional output is stored under
 `TransformerData.model_output`. More details are in the
 [TransformerModel docs](/api/architectures#TransformerModel). The training speed

website/meta/site.json

@@ -23,7 +23,6 @@
     },
     "docSearch": {
         "appId": "Y1LB128RON",
-        "apiKey": "bb601a1daab73e2dc66faf2b79564807",
         "indexName": "spacy"
     },
     "binderUrl": "explosion/spacy-io-binder",

website/next.config.mjs

@@ -32,6 +32,9 @@ const nextConfig = withPWA(
             ignoreBuildErrors: true,
         },
         images: { unoptimized: true },
+        env: {
+            DOCSEARCH_API_KEY: process.env.DOCSEARCH_API_KEY
+        }
     })
 )
 

website/src/components/search.js

@@ -1,4 +1,4 @@
-import React, { useEffect, useState } from 'react'
+import React from 'react'
 import PropTypes from 'prop-types'
 import { DocSearch } from '@docsearch/react'
 import '@docsearch/css'
@@ -6,7 +6,8 @@ import '@docsearch/css'
 import siteMetadata from '../../meta/site.json'
 
 export default function Search({ placeholder = 'Search docs' }) {
-    const { apiKey, indexName, appId } = siteMetadata.docSearch
+    const apiKey = process.env.DOCSEARCH_API_KEY
+    const { indexName, appId } = siteMetadata.docSearch
     return (
         <DocSearch appId={appId} indexName={indexName} apiKey={apiKey} placeholder={placeholder} />
     )

website/src/styles/code.module.sass

@@ -109,6 +109,8 @@
     box-shadow: inset 1px 1px 1px rgba(0, 0, 0, 0.25)
     background: var(--color-dark)
     margin: 1.5rem 0 0 2rem
+    position: sticky
+    left: 2rem
 
 .header
     width: 100%