lint

* Add workflow files for cibuildwheel

* Add config for cibuildwheel

* Set version for experimental prerelease

* Try updating cython

* Skip 32-bit windows builds

* Revert "Try updating cython"

This reverts commit c1b794ab5c.

* Try to import cibuildwheel settings from previous setup

.github/workflows/cibuildwheel.yml

@@ -0,0 +1,92 @@
+name: Build
+
+on:
+  push:
+    tags:
+      # ytf did they invent their own syntax that's almost regex?
+      # ** matches 'zero or more of any character'
+      - 'release-v[0-9]+.[0-9]+.[0-9]+**'
+      - 'prerelease-v[0-9]+.[0-9]+.[0-9]+**'
+jobs:
+  build_wheels:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        # macos-13 is an intel runner, macos-14 is apple silicon
+        os: [ubuntu-latest, windows-latest, macos-13]
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Build wheels
+        uses: pypa/cibuildwheel@v2.19.1
+        env:
+          CIBW_SOME_OPTION: value
+        with:
+          package-dir: .
+          output-dir: wheelhouse
+          config-file: "{package}/pyproject.toml"
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
+          path: ./wheelhouse/*.whl
+
+  build_sdist:
+    name: Build source distribution
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Build sdist
+        run: pipx run build --sdist
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cibw-sdist
+          path: dist/*.tar.gz
+  create_release:
+    needs: [build_wheels, build_sdist]
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      checks: write
+      actions: read
+      issues: read
+      packages: write
+      pull-requests: read
+      repository-projects: read
+      statuses: read
+    steps:
+      - name: Get the tag name and determine if it's a prerelease
+        id: get_tag_info
+        run: |
+          FULL_TAG=${GITHUB_REF#refs/tags/}
+          if [[ $FULL_TAG == release-* ]]; then
+            TAG_NAME=${FULL_TAG#release-}
+            IS_PRERELEASE=false
+          elif [[ $FULL_TAG == prerelease-* ]]; then
+            TAG_NAME=${FULL_TAG#prerelease-}
+            IS_PRERELEASE=true
+          else
+            echo "Tag does not match expected patterns" >&2
+            exit 1
+          fi
+          echo "FULL_TAG=$TAG_NAME" >> $GITHUB_ENV
+          echo "TAG_NAME=$TAG_NAME" >> $GITHUB_ENV
+          echo "IS_PRERELEASE=$IS_PRERELEASE" >> $GITHUB_ENV
+      - uses: actions/download-artifact@v4
+        with:
+          # unpacks all CIBW artifacts into dist/
+          pattern: cibw-*
+          path: dist
+          merge-multiple: true
+      - name: Create Draft Release
+        id: create_release
+        uses: softprops/action-gh-release@v2
+        if: startsWith(github.ref, 'refs/tags/')
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          name: ${{ env.TAG_NAME }}
+          draft: true
+          prerelease: ${{ env.IS_PRERELEASE }}
+          files: "./dist/*" 

.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/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/publish_pypi.yml

@@ -0,0 +1,29 @@
+# The cibuildwheel action triggers on creation of a release, this
+# triggers on publication.
+# The expected workflow is to create a draft release and let the wheels
+# upload, and then hit 'publish', which uploads to PyPi.
+
+on:
+  release:
+    types:
+      - published
+
+jobs:
+  upload_pypi:
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/spacy
+    permissions:
+      id-token: write
+      contents: read
+    if: github.event_name == 'release' && github.event.action == 'published'
+    # or, alternatively, upload to PyPI on every tag starting with 'v' (remove on: release above to use this)
+    # if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+    steps:
+      - uses: robinraju/release-downloader@v1
+        with:
+          tag: ${{ github.event.release.tag_name }}
+          fileName: '*'
+          out-file-path: 'dist'
+      - uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/slowtests.yml.disabled

@@ -14,7 +14,7 @@ jobs:
     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: "3.7"
-          architecture: x64
 
       - name: black
         run: |
@@ -75,13 +74,12 @@ 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: "3.7"
-          architecture: x64
 
       - name: Validate website/meta/universe.json
         run: |

CONTRIBUTING.md

@@ -452,10 +452,9 @@ and plugins in spaCy v3.0, and we can't wait to see what you build with it!
   spaCy website. If you're sharing your project on Twitter, feel free to tag
   [@spacy_io](https://twitter.com/spacy_io) so we can check it out.
 
-- Once your extension is published, you can open an issue on the
-  [issue tracker](https://github.com/explosion/spacy/issues) to suggest it for the
-  [resources directory](https://spacy.io/usage/resources#extensions) on the
-  website.
+- Once your extension is published, you can open a
+  [PR](https://github.com/explosion/spaCy/pulls) to suggest it for the
+  [Universe](https://spacy.io/universe) page.
 
 📖 **For more tips and best practices, see the [checklist for developing spaCy extensions](https://spacy.io/usage/processing-pipelines#extensions).**
 

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

pyproject.toml

@@ -11,5 +11,58 @@ requires = [
 ]
 build-backend = "setuptools.build_meta"
 
+[tool.cibuildwheel]
+build = "*"
+skip = "pp* cp36* cp37* cp38* *-win32"
+test-skip = ""
+free-threaded-support = false
+
+archs = ["native"]
+
+build-frontend = "default"
+config-settings = {}
+dependency-versions = "pinned"
+environment = { PIP_CONSTRAINT = "build-constraints.txt" }
+
+environment-pass = []
+build-verbosity = 0
+
+before-all = "curl https://sh.rustup.rs -sSf | sh -s -- -y --profile minimal --default-toolchain stable"
+before-build = "pip install -r requirements.txt && python setup.py clean"
+repair-wheel-command = ""
+
+test-command = ""
+before-test = ""
+test-requires = []
+test-extras = []
+
+container-engine = "docker"
+
+manylinux-x86_64-image = "manylinux2014"
+manylinux-i686-image = "manylinux2014"
+manylinux-aarch64-image = "manylinux2014"
+manylinux-ppc64le-image = "manylinux2014"
+manylinux-s390x-image = "manylinux2014"
+manylinux-pypy_x86_64-image = "manylinux2014"
+manylinux-pypy_i686-image = "manylinux2014"
+manylinux-pypy_aarch64-image = "manylinux2014"
+
+musllinux-x86_64-image = "musllinux_1_2"
+musllinux-i686-image = "musllinux_1_2"
+musllinux-aarch64-image = "musllinux_1_2"
+musllinux-ppc64le-image = "musllinux_1_2"
+musllinux-s390x-image = "musllinux_1_2"
+
+[tool.cibuildwheel.linux]
+repair-wheel-command = "auditwheel repair -w {dest_dir} {wheel}"
+
+[tool.cibuildwheel.macos]
+repair-wheel-command = "delocate-wheel --require-archs {delocate_archs} -w {dest_dir} -v {wheel}"
+
+[tool.cibuildwheel.windows]
+
+[tool.cibuildwheel.pyodide]
+
+
 [tool.isort]
 profile = "black"

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
-smart-open>=5.2.1,<7.0.0
-weasel>=0.1.0,<0.4.0
+typer>=0.3.0,<1.0.0
+weasel>=0.1.0,<0.5.0
 # Third party dependencies
 numpy>=1.15.0; python_version < "3.9"
 numpy>=1.19.0; python_version >= "3.9"
@@ -23,7 +22,6 @@ langcodes>=3.2.0,<4.0.0
 # Official Python utilities
 setuptools
 packaging>=20.0
-typing_extensions>=3.7.4.1,<4.5.0; python_version < "3.8"
 # Development dependencies
 pre-commit>=2.13.0
 cython>=0.25,<3.0

setup.cfg

@@ -22,6 +22,7 @@ classifiers =
     Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
     Programming Language :: Python :: 3.11
+    Programming Language :: Python :: 3.12
     Topic :: Scientific/Engineering
 project_urls =
     Release notes = https://github.com/explosion/spaCy/releases
@@ -53,10 +54,9 @@ 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
-    smart-open>=5.2.1,<7.0.0
+    typer>=0.3.0,<1.0.0
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0; python_version < "3.9"
     numpy>=1.19.0; python_version >= "3.9"
@@ -66,7 +66,6 @@ install_requires =
     # Official Python utilities
     setuptools
     packaging>=20.0
-    typing_extensions>=3.7.4.1,<4.5.0; python_version < "3.8"
     langcodes>=3.2.0,<4.0.0
 
 [options.entry_points]

spacy/about.py

@@ -1,5 +1,5 @@
 # fmt: off
 __title__ = "spacy"
-__version__ = "3.7.2"
+__version__ = "3.7.6"
 __download_url__ = "https://github.com/explosion/spacy-models/releases/download"
 __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

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
@@ -63,6 +64,13 @@ 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]
@@ -153,7 +161,16 @@ 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

@@ -220,6 +220,7 @@ 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")
 
 
 class Errors(metaclass=ErrorsWithCodes):

spacy/language.py

@@ -1716,6 +1716,7 @@ class Language:
             # is done, so that they can exit gracefully.
             for q in texts_q:
                 q.put(_WORK_DONE_SENTINEL)
+                q.close()
 
             # Otherwise, we are stopping because the error handler raised an
             # exception. The sentinel will be last to go out of the queue.
@@ -1729,6 +1730,9 @@ 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.
@@ -2347,6 +2351,8 @@ def _apply_pipes(
 
             # Stop working if we encounter the end-of-work sentinel.
             if isinstance(texts_with_ctx, _WorkDoneSentinel):
+                sender.close()
+                receiver.close()
                 return
 
             docs = (
@@ -2371,6 +2377,8 @@ def _apply_pipes(
             # Parent has closed the pipe prematurely. This happens when a
             # worker encounters an error and the error handler is set to
             # stop processing.
+            sender.close()
+            receiver.close()
             return
 
 

spacy/lexeme.pyx

@@ -164,45 +164,48 @@ 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)
+        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):
-            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)
+    @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
+        return self.c.id
 
-        def __set__(self, value):
-            self.c.id = value
+    @rank.setter
+    def rank(self, value):
+        self.c.id = value
 
-    property sentiment:
+    @property
+    def sentiment(self):
         """RETURNS (float): A scalar value indicating the positivity or
             negativity of the lexeme."""
-        def __get__(self):
-            sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment", {})
-            return sentiment_table.get(self.c.orth, 0.0)
+        sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment", {})
+        return sentiment_table.get(self.c.orth, 0.0)
 
-        def __set__(self, float x):
-            if "lexeme_sentiment" not in self.vocab.lookups:
-                self.vocab.lookups.add_table("lexeme_sentiment")
-            sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment")
-            sentiment_table[self.c.orth] = x
+    @sentiment.setter
+    def sentiment(self, float x):
+        if "lexeme_sentiment" not in self.vocab.lookups:
+            self.vocab.lookups.add_table("lexeme_sentiment")
+        sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment")
+        sentiment_table[self.c.orth] = x
 
     @property
     def orth_(self):
@@ -216,306 +219,338 @@ 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
+        return self.c.lower
 
-        def __set__(self, attr_t x):
-            self.c.lower = 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
+        return self.c.norm
 
-        def __set__(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
+    @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
+        return self.c.shape
 
-        def __set__(self, attr_t x):
-            self.c.shape = 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
+        return self.c.prefix
 
-        def __set__(self, attr_t x):
-            self.c.prefix = 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
+        return self.c.suffix
 
-        def __set__(self, attr_t x):
-            self.c.suffix = 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)
+        cluster_table = self.vocab.lookups.get_table("lexeme_cluster", {})
+        return cluster_table.get(self.c.orth, 0)
 
-        def __set__(self, int x):
-            cluster_table = self.vocab.lookups.get_table("lexeme_cluster", {})
-            cluster_table[self.c.orth] = 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
+        return self.c.lang
 
-        def __set__(self, attr_t x):
-            self.c.lang = 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)
+        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_table = self.vocab.lookups.get_table("lexeme_prob", {})
-            prob_table[self.c.orth] = 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]
+        return self.vocab.strings[self.c.lower]
 
-        def __set__(self, str x):
-            self.c.lower = self.vocab.strings.add(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]
+        return self.vocab.strings[self.c.norm]
 
-        def __set__(self, str x):
-            self.norm = self.vocab.strings.add(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]
+        return self.vocab.strings[self.c.shape]
 
-        def __set__(self, str x):
-            self.c.shape = self.vocab.strings.add(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]
+        return self.vocab.strings[self.c.prefix]
 
-        def __set__(self, str x):
-            self.c.prefix = self.vocab.strings.add(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]
+        return self.vocab.strings[self.c.suffix]
 
-        def __set__(self, str x):
-            self.c.suffix = self.vocab.strings.add(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]
+        return self.vocab.strings[self.c.lang]
 
-        def __set__(self, str x):
-            self.c.lang = self.vocab.strings.add(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
+        return self.c.flags
 
-        def __set__(self, flags_t x):
-            self.c.flags = x
+    @flags.setter
+    def flags(self, flags_t x):
+        self.c.flags = x
 
     @property
     def is_oov(self):
         """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)
+        return Lexeme.c_check_flag(self.c, IS_STOP)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_STOP, 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)
+        return Lexeme.c_check_flag(self.c, IS_ALPHA)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_ALPHA, 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)
+        return Lexeme.c_check_flag(self.c, IS_ASCII)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_ASCII, 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)
+        return Lexeme.c_check_flag(self.c, IS_DIGIT)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_DIGIT, 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)
+        return Lexeme.c_check_flag(self.c, IS_LOWER)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_LOWER, 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)
+        return Lexeme.c_check_flag(self.c, IS_UPPER)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_UPPER, 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)
+        return Lexeme.c_check_flag(self.c, IS_TITLE)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_TITLE, 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)
+        return Lexeme.c_check_flag(self.c, IS_PUNCT)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_PUNCT, 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)
+        return Lexeme.c_check_flag(self.c, IS_SPACE)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_SPACE, 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)
+        return Lexeme.c_check_flag(self.c, IS_BRACKET)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_BRACKET, 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)
+        return Lexeme.c_check_flag(self.c, IS_QUOTE)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_QUOTE, 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)
+        return Lexeme.c_check_flag(self.c, IS_LEFT_PUNCT)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_LEFT_PUNCT, 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)
+        return Lexeme.c_check_flag(self.c, IS_RIGHT_PUNCT)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_RIGHT_PUNCT, 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)
+        return Lexeme.c_check_flag(self.c, IS_CURRENCY)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, IS_CURRENCY, 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)
+        return Lexeme.c_check_flag(self.c, LIKE_URL)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, LIKE_URL, 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)
+        return Lexeme.c_check_flag(self.c, LIKE_NUM)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, LIKE_NUM, 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)
+        return Lexeme.c_check_flag(self.c, LIKE_EMAIL)
 
-        def __set__(self, bint x):
-            Lexeme.c_set_flag(self.c, LIKE_EMAIL, x)
+    @like_email.setter
+    def like_email(self, bint x):
+        Lexeme.c_set_flag(self.c, LIKE_EMAIL, x)

spacy/ml/models/textcat.py

@@ -185,6 +185,11 @@ def build_text_classifier_v2(
 
 
 def init_ensemble_textcat(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("maxout_layer").set_dim("nO", tok2vec_width)
@@ -264,6 +269,7 @@ 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)
 
@@ -271,10 +277,17 @@ 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)
+    model.get_ref("key_transform").set_dim("nI", tok2vec_width)
+    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

@@ -11,7 +11,6 @@ from .. import util
 from ..errors import Errors
 from ..kb import Candidate, KnowledgeBase
 from ..language import Language
-from ..ml import empty_kb
 from ..scorer import Scorer
 from ..tokens import Doc, Span
 from ..training import Example, validate_examples, validate_get_examples
@@ -105,7 +104,7 @@ def make_entity_linker(
         ): Function that produces a list of candidates, given a certain knowledge base and several 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.
     candidates_batch_size (int): Size of batches for entity candidate generation.
     threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
@@ -235,7 +234,6 @@ 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.candidates_batch_size = candidates_batch_size
         self.threshold = threshold
@@ -243,6 +241,37 @@ class EntityLinker(TrainablePipe):
         if candidates_batch_size < 1:
             raise ValueError(Errors.E1044)
 
+        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."""
@@ -284,11 +313,9 @@ 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)
@@ -354,31 +381,17 @@ 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
         )
@@ -386,11 +399,13 @@ 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/strings.pxd

@@ -25,5 +25,8 @@ cdef class StringStore:
     cdef vector[hash_t] keys
     cdef public PreshMap _map
 
-    cdef const Utf8Str* intern_unicode(self, str py_string)
-    cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length, hash_t* precalculated_hash)
+    cdef const Utf8Str* intern_unicode(self, str py_string, bint allow_transient)
+    cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length, hash_t* precalculated_hash, bint allow_transient) 
+    cdef vector[hash_t] _transient_keys
+    cdef PreshMap _transient_map
+    cdef Pool _non_temp_mem

spacy/strings.pyx

@@ -1,6 +1,10 @@
 # cython: infer_types=True
 # cython: profile=False
 cimport cython
+
+from contextlib import contextmanager
+from typing import Iterator, List, Optional
+
 from libc.stdint cimport uint32_t
 from libc.string cimport memcpy
 from murmurhash.mrmr cimport hash32, hash64
@@ -31,7 +35,7 @@ def get_string_id(key):
     This function optimises for convenience over performance, so shouldn't be
     used in tight loops.
     """
-    cdef hash_t str_hash    
+    cdef hash_t str_hash
     if isinstance(key, str):
         if len(key) == 0:
             return 0
@@ -45,8 +49,8 @@ def get_string_id(key):
     elif _try_coerce_to_hash(key, &str_hash):
         # Coerce the integral key to the expected primitive hash type.
         # This ensures that custom/overloaded "primitive" data types
-        # such as those implemented by numpy are not inadvertently used 
-        # downsteam (as these are internally implemented as custom PyObjects 
+        # such as those implemented by numpy are not inadvertently used
+        # downsteam (as these are internally implemented as custom PyObjects
         # whose comparison operators can incur a significant overhead).
         return str_hash
     else:
@@ -119,7 +123,9 @@ cdef class StringStore:
         strings (iterable): A sequence of unicode strings to add to the store.
         """
         self.mem = Pool()
+        self._non_temp_mem = self.mem
         self._map = PreshMap()
+        self._transient_map = None
         if strings is not None:
             for string in strings:
                 self.add(string)
@@ -152,10 +158,13 @@ cdef class StringStore:
                 return SYMBOLS_BY_INT[str_hash]
             else:
                 utf8str = <Utf8Str*>self._map.get(str_hash)
+                if utf8str is NULL and self._transient_map is not None:
+                    utf8str = <Utf8Str*>self._transient_map.get(str_hash)
         else:
             # TODO: Raise an error instead
             utf8str = <Utf8Str*>self._map.get(string_or_id)
-
+            if utf8str is NULL and self._transient_map is not None:
+                utf8str = <Utf8Str*>self._transient_map.get(str_hash)
         if utf8str is NULL:
             raise KeyError(Errors.E018.format(hash_value=string_or_id))
         else:
@@ -175,10 +184,46 @@ cdef class StringStore:
         else:
             return self[key]
 
-    def add(self, string):
+    def __reduce__(self):
+        strings = list(self.non_transient_keys())
+        return (StringStore, (strings,), None, None, None)
+
+    def __len__(self) -> int:
+        """The number of strings in the store.
+
+        RETURNS (int): The number of strings in the store.
+        """
+        return self._keys.size() + self._transient_keys.size()
+
+    @contextmanager
+    def memory_zone(self, mem: Optional[Pool] = None) -> Pool:
+        """Begin a block where all resources allocated during the block will
+        be freed at the end of it. If a resources was created within the
+        memory zone block, accessing it outside the block is invalid.
+        Behaviour of this invalid access is undefined. Memory zones should
+        not be nested.
+
+        The memory zone is helpful for services that need to process large
+        volumes of text with a defined memory budget.
+        """
+        if mem is None:
+            mem = Pool()
+        self.mem = mem
+        self._transient_map = PreshMap()
+        yield mem
+        self.mem = self._non_temp_mem
+        self._transient_map = None
+        self._transient_keys.clear()
+
+    def add(self, string: str, allow_transient: bool = False) -> int:
         """Add a string to the StringStore.
 
         string (str): The string to add.
+        allow_transient (bool): Allow the string to be stored in the 'transient'
+          map, which will be flushed at the end of the memory zone. Strings
+          encountered during arbitrary text processing should be added
+          with allow_transient=True, while labels and other strings used
+          internally should not.
         RETURNS (uint64): The string's hash value.
         """
         cdef hash_t str_hash
@@ -188,22 +233,26 @@ cdef class StringStore:
 
             string = string.encode("utf8")
             str_hash = hash_utf8(string, len(string))
-            self._intern_utf8(string, len(string), &str_hash)
+            self._intern_utf8(string, len(string), &str_hash, allow_transient)
         elif isinstance(string, bytes):
             if string in SYMBOLS_BY_STR:
                 return SYMBOLS_BY_STR[string]
             str_hash = hash_utf8(string, len(string))
-            self._intern_utf8(string, len(string), &str_hash)
+            self._intern_utf8(string, len(string), &str_hash, allow_transient)
         else:
             raise TypeError(Errors.E017.format(value_type=type(string)))
         return str_hash
 
     def __len__(self):
         """The number of strings in the store.
+        if string in SYMBOLS_BY_STR:
+            return SYMBOLS_BY_STR[string]
+        else:
+            return self._intern_str(string, allow_transient)
 
         RETURNS (int): The number of strings in the store.
         """
-        return self.keys.size()
+        return self.keys.size() + self._transient_keys.size()
 
     def __contains__(self, string_or_id not None):
         """Check whether a string or ID is in the store.
@@ -222,30 +271,70 @@ cdef class StringStore:
             pass
         else:
             # TODO: Raise an error instead
-            return self._map.get(string_or_id) is not NULL
-
+            if self._map.get(string_or_id) is not NULL:
+                return True
+            elif self._transient_map is not None and self._transient_map.get(string_or_id) is not NULL:
+                return True
+            else:
+                return False
         if str_hash < len(SYMBOLS_BY_INT):
             return True
         else:
-            return self._map.get(str_hash) is not NULL
+            if self._map.get(str_hash) is not NULL:
+                return True
+            elif self._transient_map is not None and self._transient_map.get(string_or_id) is not NULL:
+                return True
+            else:
+                return False
 
     def __iter__(self):
         """Iterate over the strings in the store, in order.
 
         YIELDS (str): A string in the store.
         """
+        yield from self.non_transient_keys()
+        yield from self.transient_keys()
+
+    def non_transient_keys(self) -> Iterator[str]:
+        """Iterate over the stored strings in insertion order.
+
+        RETURNS: A list of strings.
+        """
         cdef int i
         cdef hash_t key
         for i in range(self.keys.size()):
             key = self.keys[i]
             utf8str = <Utf8Str*>self._map.get(key)
             yield decode_Utf8Str(utf8str)
-        # TODO: Iterate OOV here?
 
     def __reduce__(self):
         strings = list(self)
         return (StringStore, (strings,), None, None, None)
 
+    def transient_keys(self) -> Iterator[str]:
+        if self._transient_map is None:
+            return []
+        for i in range(self._transient_keys.size()):
+            utf8str = <Utf8Str*>self._transient_map.get(self._transient_keys[i])
+            yield decode_Utf8Str(utf8str)
+
+    def values(self) -> List[int]:
+        """Iterate over the stored strings hashes in insertion order.
+
+        RETURNS: A list of string hashs.
+        """
+        cdef int i
+        hashes = [None] * self._keys.size()
+        for i in range(self._keys.size()):
+            hashes[i] = self._keys[i]
+        if self._transient_map is not None:
+            transient_hashes = [None] * self._transient_keys.size()
+            for i in range(self._transient_keys.size()):
+                transient_hashes[i] = self._transient_keys[i]
+        else:
+            transient_hashes = []
+        return hashes + transient_hashes
+
     def to_disk(self, path):
         """Save the current state to a directory.
 
@@ -269,7 +358,7 @@ cdef class StringStore:
         prev = list(self)
         self._reset_and_load(strings)
         for word in prev:
-            self.add(word)
+            self.add(word, allow_transient=False)
         return self
 
     def to_bytes(self, **kwargs):
@@ -289,7 +378,7 @@ cdef class StringStore:
         prev = list(self)
         self._reset_and_load(strings)
         for word in prev:
-            self.add(word)
+            self.add(word, allow_transient=False)
         return self
 
     def _reset_and_load(self, strings):
@@ -297,22 +386,34 @@ cdef class StringStore:
         self._map = PreshMap()
         self.keys.clear()
         for string in strings:
-            self.add(string)
+            self.add(string, allow_transient=False)
 
-    cdef const Utf8Str* intern_unicode(self, str py_string):
+    cdef const Utf8Str* intern_unicode(self, str py_string, bint allow_transient):
         # 0 means missing, but we don't bother offsetting the index.
         cdef bytes byte_string = py_string.encode("utf8")
-        return self._intern_utf8(byte_string, len(byte_string), NULL)
+        return self._intern_utf8(byte_string, len(byte_string), NULL, allow_transient)
 
     @cython.final
-    cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length, hash_t* precalculated_hash):
+    cdef const Utf8Str* _intern_utf8(self, char* utf8_string, int length, hash_t* precalculated_hash, bint allow_transient):
         # TODO: This function's API/behaviour is an unholy mess...
         # 0 means missing, but we don't bother offsetting the index.
         cdef hash_t key = precalculated_hash[0] if precalculated_hash is not NULL else hash_utf8(utf8_string, length)
         cdef Utf8Str* value = <Utf8Str*>self._map.get(key)
         if value is not NULL:
             return value
+        if allow_transient and self._transient_map is not None:
+            # If we've already allocated a transient string, and now we
+            # want to intern it permanently, we'll end up with the string
+            # in both places. That seems fine -- I don't see why we need
+            # to remove it from the transient map.
+            value = <Utf8Str*>self._transient_map.get(key)
+            if value is not NULL:
+                return value
         value = _allocate(self.mem, <unsigned char*>utf8_string, length)
-        self._map.set(key, value)
-        self.keys.push_back(key)
+        if allow_transient and self._transient_map is not None:
+            self._transient_map.set(key, value)
+            self._transient_keys.push_back(key)
+        else:
+            self._map.set(key, value)
+            self.keys.push_back(key)
         return value

spacy/tests/pipeline/test_entity_linker.py

@@ -717,7 +717,7 @@ 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
@@ -744,7 +744,9 @@ 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
@@ -807,6 +809,107 @@ 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

@@ -28,6 +28,8 @@ 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 = [
@@ -40,6 +42,13 @@ 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 = []
@@ -546,6 +555,34 @@ 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
@@ -328,7 +329,7 @@ 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
@@ -738,9 +739,13 @@ 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:
-        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)
+        # 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)
 
 
 def test_invalid_arg_to_pipeline(nlp):

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/tests/vocab_vectors/test_memory_zone.py

@@ -0,0 +1,36 @@
+from spacy.vocab import Vocab
+
+
+def test_memory_zone_no_insertion():
+    vocab = Vocab()
+    with vocab.memory_zone():
+        pass
+    lex = vocab["horse"]
+    assert lex.text == "horse"
+
+
+def test_memory_zone_insertion():
+    vocab = Vocab()
+    _ = vocab["dog"]
+    assert "dog" in vocab
+    assert "horse" not in vocab
+    with vocab.memory_zone():
+        lex = vocab["horse"]
+        assert lex.text == "horse"
+    assert "dog" in vocab
+    assert "horse" not in vocab
+
+
+def test_memory_zone_redundant_insertion():
+    """Test that if we insert an already-existing word while
+    in the memory zone, it stays persistent"""
+    vocab = Vocab()
+    _ = vocab["dog"]
+    assert "dog" in vocab
+    assert "horse" not in vocab
+    with vocab.memory_zone():
+        lex = vocab["horse"]
+        assert lex.text == "horse"
+        _ = vocab["dog"]
+    assert "dog" in vocab
+    assert "horse" not in vocab

spacy/tokenizer.pxd

@@ -25,9 +25,7 @@ cdef class Tokenizer:
     cdef PhraseMatcher _special_matcher
     # TODO convert to bool in v4
     cdef int _faster_heuristics
-    # TODO next one is unused and should be removed in v4
-    # https://github.com/explosion/spaCy/pull/9150
-    cdef int _unused_int2
+    cdef public int max_cache_size
 
     cdef Doc _tokenize_affixes(self, str string, bint with_special_cases)
     cdef int _apply_special_cases(self, Doc doc) except -1

spacy/tokenizer.pyx

@@ -30,7 +30,7 @@ cdef class Tokenizer:
     """
     def __init__(self, Vocab vocab, rules=None, prefix_search=None,
                  suffix_search=None, infix_finditer=None, token_match=None,
-                 url_match=None, faster_heuristics=True):
+                 url_match=None, faster_heuristics=True, max_cache_size=10000):
         """Create a `Tokenizer`, to create `Doc` objects given unicode text.
 
         vocab (Vocab): A storage container for lexical types.
@@ -50,6 +50,7 @@ cdef class Tokenizer:
         faster_heuristics (bool): Whether to restrict the final
             Matcher-based pass for rules to those containing affixes or space.
             Defaults to True.
+        max_cache_size (int): Maximum number of tokenization chunks to cache.
 
         EXAMPLE:
             >>> tokenizer = Tokenizer(nlp.vocab)
@@ -69,66 +70,74 @@ cdef class Tokenizer:
         self._rules = {}
         self._special_matcher = PhraseMatcher(self.vocab)
         self._load_special_cases(rules)
+        self.max_cache_size = max_cache_size
 
-    property token_match:
-        def __get__(self):
-            return self._token_match
+    @property
+    def token_match(self):
+        return self._token_match
 
-        def __set__(self, token_match):
-            self._token_match = token_match
-            self._reload_special_cases()
+    @token_match.setter
+    def token_match(self, token_match):
+        self._token_match = token_match
+        self._reload_special_cases()
 
-    property url_match:
-        def __get__(self):
-            return self._url_match
+    @property
+    def url_match(self):
+        return self._url_match
 
-        def __set__(self, url_match):
-            self._url_match = url_match
-            self._reload_special_cases()
+    @url_match.setter
+    def url_match(self, url_match):
+        self._url_match = url_match
+        self._reload_special_cases()
 
-    property prefix_search:
-        def __get__(self):
-            return self._prefix_search
+    @property
+    def prefix_search(self):
+        return self._prefix_search
 
-        def __set__(self, prefix_search):
-            self._prefix_search = prefix_search
-            self._reload_special_cases()
+    @prefix_search.setter
+    def prefix_search(self, prefix_search):
+        self._prefix_search = prefix_search
+        self._reload_special_cases()
 
-    property suffix_search:
-        def __get__(self):
-            return self._suffix_search
+    @property
+    def suffix_search(self):
+        return self._suffix_search
 
-        def __set__(self, suffix_search):
-            self._suffix_search = suffix_search
-            self._reload_special_cases()
+    @suffix_search.setter
+    def suffix_search(self, suffix_search):
+        self._suffix_search = suffix_search
+        self._reload_special_cases()
 
-    property infix_finditer:
-        def __get__(self):
-            return self._infix_finditer
+    @property
+    def infix_finditer(self):
+        return self._infix_finditer
 
-        def __set__(self, infix_finditer):
-            self._infix_finditer = infix_finditer
-            self._reload_special_cases()
+    @infix_finditer.setter
+    def infix_finditer(self, infix_finditer):
+        self._infix_finditer = infix_finditer
+        self._reload_special_cases()
 
-    property rules:
-        def __get__(self):
-            return self._rules
+    @property
+    def rules(self):
+        return self._rules
 
-        def __set__(self, rules):
-            self._rules = {}
-            self._flush_cache()
-            self._flush_specials()
-            self._cache = PreshMap()
-            self._specials = PreshMap()
-            self._load_special_cases(rules)
+    @rules.setter
+    def rules(self, rules):
+        self._rules = {}
+        self._flush_cache()
+        self._flush_specials()
+        self._cache = PreshMap()
+        self._specials = PreshMap()
+        self._load_special_cases(rules)
 
-    property faster_heuristics:
-        def __get__(self):
-            return bool(self._faster_heuristics)
+    @property
+    def faster_heuristics(self):
+        return bool(self._faster_heuristics)
 
-        def __set__(self, faster_heuristics):
-            self._faster_heuristics = bool(faster_heuristics)
-            self._reload_special_cases()
+    @faster_heuristics.setter
+    def faster_heuristics(self, faster_heuristics):
+        self._faster_heuristics = bool(faster_heuristics)
+        self._reload_special_cases()
 
     def __reduce__(self):
         args = (self.vocab,
@@ -390,8 +399,9 @@ cdef class Tokenizer:
                                    has_special, with_special_cases)
         self._attach_tokens(tokens, span, &prefixes, &suffixes, has_special,
                             with_special_cases)
-        self._save_cached(&tokens.c[orig_size], orig_key, has_special,
-                          tokens.length - orig_size)
+        if len(self._cache) < self.max_cache_size:
+            self._save_cached(&tokens.c[orig_size], orig_key, has_special,
+                              tokens.length - orig_size)
 
     cdef str _split_affixes(
         self,
@@ -507,6 +517,9 @@ cdef class Tokenizer:
         if n <= 0:
             # avoid mem alloc of zero length
             return 0
+        # Historically this check was mostly used to avoid caching
+        # chunks that had tokens owned by the Doc. Now that that's
+        # not a thing, I don't think we need this?
         for i in range(n):
             if self.vocab._by_orth.get(tokens[i].lex.orth) == NULL:
                 return 0

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.
 
@@ -676,48 +677,49 @@ 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:
-                return self._vector
-            xp = get_array_module(self.vocab.vectors.data)
-            if not len(self):
-                self._vector = xp.zeros((self.vocab.vectors_length,), dtype="f")
-                return self._vector
-            elif self.vocab.vectors.size > 0:
-                self._vector = sum(t.vector for t in self) / len(self)
-                return self._vector
-            elif self.tensor.size > 0:
-                self._vector = self.tensor.mean(axis=0)
-                return self._vector
-            else:
-                return xp.zeros((self.vocab.vectors_length,), dtype="float32")
+        if "vector" in self.user_hooks:
+            return self.user_hooks["vector"](self)
+        if self._vector is not None:
+            return self._vector
+        xp = get_array_module(self.vocab.vectors.data)
+        if not len(self):
+            self._vector = xp.zeros((self.vocab.vectors_length,), dtype="f")
+            return self._vector
+        elif self.vocab.vectors.size > 0:
+            self._vector = sum(t.vector for t in self) / len(self)
+            return self._vector
+        elif self.tensor.size > 0:
+            self._vector = self.tensor.mean(axis=0)
+            return self._vector
+        else:
+            return xp.zeros((self.vocab.vectors_length,), dtype="float32")
 
-        def __set__(self, value):
-            self._vector = 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
-            cdef double norm = 0
-            if self._vector_norm is None:
-                norm = 0.0
-                for value in self.vector:
-                    norm += value * value
-                self._vector_norm = sqrt(norm) if norm != 0 else 0
-            return self._vector_norm
+        if "vector_norm" in self.user_hooks:
+            return self.user_hooks["vector_norm"](self)
+        cdef float value
+        cdef double norm = 0
+        if self._vector_norm is None:
+            norm = 0.0
+            for value in self.vector:
+                norm += value * value
+            self._vector_norm = sqrt(norm) if norm != 0 else 0
+        return self._vector_norm
 
-        def __set__(self, value):
-            self._vector_norm = value
+    @vector_norm.setter
+    def vector_norm(self, value):
+        self._vector_norm = value
 
     @property
     def text(self):
@@ -736,7 +738,8 @@ cdef class Doc:
         """
         return self.text
 
-    property ents:
+    @property
+    def ents(self):
         """The named entities in the document. Returns a tuple of named entity
         `Span` objects, if the entity recognizer has been applied.
 
@@ -744,55 +747,55 @@ cdef class Doc:
 
         DOCS: https://spacy.io/api/doc#ents
         """
-        def __get__(self):
-            cdef int i
-            cdef const TokenC* token
-            cdef int start = -1
-            cdef attr_t label = 0
-            cdef attr_t kb_id = 0
-            cdef attr_t ent_id = 0
-            output = []
-            for i in range(self.length):
-                token = &self.c[i]
-                if token.ent_iob == 1:
-                    if start == -1:
-                        seq = [f"{t.text}|{t.ent_iob_}" for t in self[i-5:i+5]]
-                        raise ValueError(Errors.E093.format(seq=" ".join(seq)))
-                elif token.ent_iob == 2 or token.ent_iob == 0 or \
-                        (token.ent_iob == 3 and token.ent_type == 0):
-                    if start != -1:
-                        output.append(Span(self, start, i, label=label, kb_id=kb_id, span_id=ent_id))
-                    start = -1
-                    label = 0
-                    kb_id = 0
-                    ent_id = 0
-                elif token.ent_iob == 3:
-                    if start != -1:
-                        output.append(Span(self, start, i, label=label, kb_id=kb_id, span_id=ent_id))
-                    start = i
-                    label = token.ent_type
-                    kb_id = token.ent_kb_id
-                    ent_id = token.ent_id
-            if start != -1:
-                output.append(Span(self, start, self.length, label=label, kb_id=kb_id, span_id=ent_id))
-            # remove empty-label spans
-            output = [o for o in output if o.label_ != ""]
-            return tuple(output)
+        cdef int i
+        cdef const TokenC* token
+        cdef int start = -1
+        cdef attr_t label = 0
+        cdef attr_t kb_id = 0
+        cdef attr_t ent_id = 0
+        output = []
+        for i in range(self.length):
+            token = &self.c[i]
+            if token.ent_iob == 1:
+                if start == -1:
+                    seq = [f"{t.text}|{t.ent_iob_}" for t in self[i-5:i+5]]
+                    raise ValueError(Errors.E093.format(seq=" ".join(seq)))
+            elif token.ent_iob == 2 or token.ent_iob == 0 or \
+                    (token.ent_iob == 3 and token.ent_type == 0):
+                if start != -1:
+                    output.append(Span(self, start, i, label=label, kb_id=kb_id, span_id=ent_id))
+                start = -1
+                label = 0
+                kb_id = 0
+                ent_id = 0
+            elif token.ent_iob == 3:
+                if start != -1:
+                    output.append(Span(self, start, i, label=label, kb_id=kb_id, span_id=ent_id))
+                start = i
+                label = token.ent_type
+                kb_id = token.ent_kb_id
+                ent_id = token.ent_id
+        if start != -1:
+            output.append(Span(self, start, self.length, label=label, kb_id=kb_id, span_id=ent_id))
+        # remove empty-label spans
+        output = [o for o in output if o.label_ != ""]
+        return tuple(output)
 
-        def __set__(self, ents):
-            # TODO:
-            # 1. Test basic data-driven ORTH gazetteer
-            # 2. Test more nuanced date and currency regex
-            cdef attr_t kb_id, ent_id
-            cdef int ent_start, ent_end
-            ent_spans = []
-            for ent_info in ents:
-                entity_type_, kb_id, ent_start, ent_end, ent_id = get_entity_info(ent_info)
-                if isinstance(entity_type_, str):
-                    self.vocab.strings.add(entity_type_)
-                span = Span(self, ent_start, ent_end, label=entity_type_, kb_id=kb_id, span_id=ent_id)
-                ent_spans.append(span)
-            self.set_ents(ent_spans, default=SetEntsDefault.outside)
+    @ents.setter
+    def ents(self, ents):
+        # TODO:
+        # 1. Test basic data-driven ORTH gazetteer
+        # 2. Test more nuanced date and currency regex
+        cdef attr_t kb_id, ent_id
+        cdef int ent_start, ent_end
+        ent_spans = []
+        for ent_info in ents:
+            entity_type_, kb_id, ent_start, ent_end, ent_id = get_entity_info(ent_info)
+            if isinstance(entity_type_, str):
+                self.vocab.strings.add(entity_type_)
+            span = Span(self, ent_start, ent_end, label=entity_type_, kb_id=kb_id, span_id=ent_id)
+            ent_spans.append(span)
+        self.set_ents(ent_spans, default=SetEntsDefault.outside)
 
     def set_ents(self, entities, *, blocked=None, missing=None, outside=None, default=SetEntsDefault.outside):
         """Set entity annotation.

spacy/tokens/span.pyx

@@ -757,78 +757,87 @@ cdef class Span:
         for word in self.rights:
             yield from word.subtree
 
-    property start:
-        def __get__(self):
-            return self.c.start
+    @property
+    def start(self):
+        return self.c.start
 
-        def __set__(self, int start):
-            if start < 0:
-                raise IndexError(Errors.E1032.format(var="start", forbidden="< 0", value=start))
-            self.c.start = start
+    @start.setter
+    def start(self, int start):
+        if start < 0:
+            raise IndexError(Errors.E1032.format(var="start", forbidden="< 0", value=start))
+        self.c.start = start
 
-    property end:
-        def __get__(self):
-            return self.c.end
+    @property
+    def end(self):
+        return self.c.end
 
-        def __set__(self, int end):
-            if end < 0:
-                raise IndexError(Errors.E1032.format(var="end", forbidden="< 0", value=end))
-            self.c.end = end
+    @end.setter
+    def end(self, int end):
+        if end < 0:
+            raise IndexError(Errors.E1032.format(var="end", forbidden="< 0", value=end))
+        self.c.end = end
 
-    property start_char:
-        def __get__(self):
-            return self.c.start_char
+    @property
+    def start_char(self):
+        return self.c.start_char
 
-        def __set__(self, int start_char):
-            if start_char < 0:
-                raise IndexError(Errors.E1032.format(var="start_char", forbidden="< 0", value=start_char))
-            self.c.start_char = start_char
+    @start_char.setter
+    def start_char(self, int start_char):
+        if start_char < 0:
+            raise IndexError(Errors.E1032.format(var="start_char", forbidden="< 0", value=start_char))
+        self.c.start_char = start_char
 
-    property end_char:
-        def __get__(self):
-            return self.c.end_char
+    @property
+    def end_char(self):
+        return self.c.end_char
 
-        def __set__(self, int end_char):
-            if end_char < 0:
-                raise IndexError(Errors.E1032.format(var="end_char", forbidden="< 0", value=end_char))
-            self.c.end_char = end_char
+    @end_char.setter
+    def end_char(self, int end_char):
+        if end_char < 0:
+            raise IndexError(Errors.E1032.format(var="end_char", forbidden="< 0", value=end_char))
+        self.c.end_char = end_char
 
-    property label:
-        def __get__(self):
-            return self.c.label
+    @property
+    def label(self):
+        return self.c.label
 
-        def __set__(self, attr_t label):
-            self.c.label = label
+    @label.setter
+    def label(self, attr_t label):
+        self.c.label = label
 
-    property kb_id:
-        def __get__(self):
-            return self.c.kb_id
+    @property
+    def kb_id(self):
+        return self.c.kb_id
 
-        def __set__(self, attr_t kb_id):
-            self.c.kb_id = kb_id
+    @kb_id.setter
+    def kb_id(self, attr_t kb_id):
+        self.c.kb_id = kb_id
 
-    property id:
-        def __get__(self):
-            return self.c.id
+    @property
+    def id(self):
+        return self.c.id
 
-        def __set__(self, attr_t id):
-            self.c.id = id
+    @id.setter
+    def id(self, attr_t id):
+        self.c.id = id
 
-    property ent_id:
+    @property
+    def ent_id(self):
         """RETURNS (uint64): The entity ID."""
-        def __get__(self):
-            return self.root.ent_id
+        return self.root.ent_id
 
-        def __set__(self, hash_t key):
-            raise NotImplementedError(Errors.E200.format(attr="ent_id"))
+    @ent_id.setter
+    def ent_id(self, hash_t key):
+        raise NotImplementedError(Errors.E200.format(attr="ent_id"))
 
-    property ent_id_:
+    @property
+    def ent_id_(self):
         """RETURNS (str): The (string) entity ID."""
-        def __get__(self):
-            return self.root.ent_id_
+        return self.root.ent_id_
 
-        def __set__(self, str key):
-            raise NotImplementedError(Errors.E200.format(attr="ent_id_"))
+    @ent_id_.setter
+    def ent_id_(self, str key):
+        raise NotImplementedError(Errors.E200.format(attr="ent_id_"))
 
     @property
     def orth_(self):
@@ -843,29 +852,32 @@ 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):
         """RETURNS (str): The span's label."""
-        def __get__(self):
-            return self.doc.vocab.strings[self.label]
+        return self.doc.vocab.strings[self.label]
 
-        def __set__(self, str label_):
-            self.label = self.doc.vocab.strings.add(label_)
+    @label_.setter
+    def label_(self, str label_):
+        self.label = self.doc.vocab.strings.add(label_)
 
-    property kb_id_:
+    @property
+    def kb_id_(self):
         """RETURNS (str): The span's KB ID."""
-        def __get__(self):
-            return self.doc.vocab.strings[self.kb_id]
+        return self.doc.vocab.strings[self.kb_id]
 
-        def __set__(self, str kb_id_):
-            self.kb_id = self.doc.vocab.strings.add(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):
         """RETURNS (str): The span's ID."""
-        def __get__(self):
-            return self.doc.vocab.strings[self.id]
+        return self.doc.vocab.strings[self.id]
 
-        def __set__(self, str id_):
-            self.id = self.doc.vocab.strings.add(id_)
+    @id_.setter
+    def id_(self, str id_):
+        self.id = self.doc.vocab.strings.add(id_)
 
 
 cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:

spacy/tokens/token.pyx

@@ -249,15 +249,16 @@ cdef class Token:
         """
         return not self.c.morph == 0
 
-    property morph:
-        def __get__(self):
-            return MorphAnalysis.from_id(self.vocab, self.c.morph)
+    @property
+    def morph(self):
+        return MorphAnalysis.from_id(self.vocab, self.c.morph)
 
-        def __set__(self, MorphAnalysis morph):
-            # Check that the morph has the same vocab
-            if self.vocab != morph.vocab:
-                raise ValueError(Errors.E1013)
-            self.c.morph = morph.c.key
+    @morph.setter
+    def morph(self, MorphAnalysis morph):
+        # Check that the morph has the same vocab
+        if self.vocab != morph.vocab:
+            raise ValueError(Errors.E1013)
+        self.c.morph = morph.c.key
 
     def set_morph(self, features):
         cdef hash_t key
@@ -377,39 +378,43 @@ 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
+        return self.c.lemma
 
-        def __set__(self, attr_t lemma):
-            self.c.lemma = 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
+        return self.c.pos
 
-        def __set__(self, pos):
-            self.c.pos = 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
+        return self.c.tag
 
-        def __set__(self, attr_t tag):
-            self.c.tag = 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
+        return self.c.dep
 
-        def __set__(self, attr_t label):
-            self.c.dep = label
+    @dep.setter
+    def dep(self, attr_t label):
+        self.c.dep = label
 
     @property
     def has_vector(self):
@@ -494,48 +499,51 @@ cdef class Token:
             return self.doc.user_token_hooks["sent"](self)
         return self.doc[self.i : self.i+1].sent
 
-    property sent_start:
-        def __get__(self):
-            """Deprecated: use Token.is_sent_start instead."""
-            # Raising a deprecation warning here causes errors for autocomplete
-            # Handle broken backwards compatibility case: doc[0].sent_start
-            # was False.
-            if self.i == 0:
-                return False
-            else:
-                return self.c.sent_start
+    @property
+    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
+        # was False.
+        if self.i == 0:
+            return False
+        else:
+            return self.c.sent_start
 
-        def __set__(self, value):
-            self.is_sent_start = 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:
-                return False
-            else:
-                return True
+        if self.c.sent_start == 0:
+            return None
+        elif self.c.sent_start < 0:
+            return False
+        else:
+            return True
 
-        def __set__(self, value):
-            if self.doc.has_annotation("DEP"):
-                raise ValueError(Errors.E043)
-            if value is None:
-                self.c.sent_start = 0
-            elif value is True:
-                self.c.sent_start = 1
-            elif value is False:
-                self.c.sent_start = -1
-            else:
-                raise ValueError(Errors.E044.format(value=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:
+            self.c.sent_start = 0
+        elif value is True:
+            self.c.sent_start = 1
+        elif value is False:
+            self.c.sent_start = -1
+        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`.
 
@@ -544,18 +552,18 @@ 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:
-                return None
-            elif self.doc[self.i+1].is_sent_start is True:
-                return True
-            else:
-                return False
+        if self.i + 1 == len(self.doc):
+            return True
+        elif self.doc[self.i+1].is_sent_start is None:
+            return None
+        elif self.doc[self.i+1].is_sent_start is True:
+            return True
+        else:
+            return False
 
-        def __set__(self, value):
-            raise ValueError(Errors.E196)
+    @is_sent_end.setter
+    def is_sent_end(self, value):
+        raise ValueError(Errors.E196)
 
     @property
     def lefts(self):
@@ -682,41 +690,42 @@ 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]
+        if not self.has_head():
+            return self
+        else:
+            return self.doc[self.i + self.c.head]
 
-        def __set__(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
-            # Check that token is from the same document
-            if self.doc != new_head.doc:
-                raise ValueError(Errors.E191)
-            # Do nothing if old head is new head
-            if self.i + self.c.head == new_head.i:
-                return
-            # Find the widest l/r_edges of the roots of the two tokens involved
-            # to limit the number of tokens for set_children_from_heads
-            cdef Token self_root, new_head_root
-            self_root = ([self] + list(self.ancestors))[-1]
-            new_head_ancestors = list(new_head.ancestors)
-            new_head_root = new_head_ancestors[-1] if new_head_ancestors else new_head
-            start = self_root.c.l_edge if self_root.c.l_edge < new_head_root.c.l_edge else new_head_root.c.l_edge
-            end = self_root.c.r_edge if self_root.c.r_edge > new_head_root.c.r_edge else new_head_root.c.r_edge
-            # Set new head
-            self.c.head = new_head.i - self.i
-            # Adjust parse properties and sentence starts
-            set_children_from_heads(self.doc.c, start, end + 1)
+    @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
+        # Check that token is from the same document
+        if self.doc != new_head.doc:
+            raise ValueError(Errors.E191)
+        # Do nothing if old head is new head
+        if self.i + self.c.head == new_head.i:
+            return
+        # Find the widest l/r_edges of the roots of the two tokens involved
+        # to limit the number of tokens for set_children_from_heads
+        cdef Token self_root, new_head_root
+        self_root = ([self] + list(self.ancestors))[-1]
+        new_head_ancestors = list(new_head.ancestors)
+        new_head_root = new_head_ancestors[-1] if new_head_ancestors else new_head
+        start = self_root.c.l_edge if self_root.c.l_edge < new_head_root.c.l_edge else new_head_root.c.l_edge
+        end = self_root.c.r_edge if self_root.c.r_edge > new_head_root.c.r_edge else new_head_root.c.r_edge
+        # Set new head
+        self.c.head = new_head.i - self.i
+        # Adjust parse properties and sentence starts
+        set_children_from_heads(self.doc.c, start, end + 1)
 
     @property
     def conjuncts(self):
@@ -744,21 +753,23 @@ 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
+        return self.c.ent_type
 
-        def __set__(self, ent_type):
-            self.c.ent_type = 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]
+        return self.vocab.strings[self.c.ent_type]
 
-        def __set__(self, ent_type):
-            self.c.ent_type = self.vocab.strings.add(ent_type)
+    @ent_type_.setter
+    def ent_type_(self, ent_type):
+        self.c.ent_type = self.vocab.strings.add(ent_type)
 
     @property
     def ent_iob(self):
@@ -784,41 +795,45 @@ 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
+        return self.c.ent_id
 
-        def __set__(self, hash_t key):
-            self.c.ent_id = 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]
+        return self.vocab.strings[self.c.ent_id]
 
-        def __set__(self, name):
-            self.c.ent_id = self.vocab.strings.add(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
+        return self.c.ent_kb_id
 
-        def __set__(self, attr_t ent_kb_id):
-            self.c.ent_kb_id = 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]
+        return self.vocab.strings[self.c.ent_kb_id]
 
-        def __set__(self, ent_kb_id):
-            self.c.ent_kb_id = self.vocab.strings.add(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
     def whitespace_(self):
@@ -840,16 +855,17 @@ 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]
+        return self.vocab.strings[self.norm]
 
-        def __set__(self, str norm_):
-            self.c.norm = self.vocab.strings.add(norm_)
+    @norm_.setter
+    def norm_(self, str norm_):
+        self.c.norm = self.vocab.strings.add(norm_)
 
     @property
     def shape_(self):
@@ -879,33 +895,36 @@ 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]
+        return self.vocab.strings[self.c.lemma]
 
-        def __set__(self, str lemma_):
-            self.c.lemma = self.vocab.strings.add(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]
+        return parts_of_speech.NAMES[self.c.pos]
 
-        def __set__(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]
+    @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]
+        return self.vocab.strings[self.c.tag]
 
-        def __set__(self, tag):
-            self.tag = self.vocab.strings.add(tag)
+    @tag_.setter
+    def tag_(self, tag):
+        self.tag = self.vocab.strings.add(tag)
 
     def has_dep(self):
         """Check whether the token has annotated dep information.
@@ -915,13 +934,14 @@ 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]
+        return self.vocab.strings[self.c.dep]
 
-        def __set__(self, str label):
-            self.c.dep = self.vocab.strings.add(label)
+    @dep_.setter
+    def dep_(self, str label):
+        self.c.dep = self.vocab.strings.add(label)
 
     @property
     def is_oov(self):

spacy/training/example.pyx

@@ -88,23 +88,25 @@ cdef class Example:
     def __len__(self):
         return len(self.predicted)
 
-    property predicted:
-        def __get__(self):
-            return self.x
+    @property
+    def predicted(self):
+        return self.x
 
-        def __set__(self, doc):
-            self.x = doc
-            self._cached_alignment = None
-            self._cached_words_x = [t.text for t in 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:
-        def __get__(self):
-            return self.y
+    @property
+    def reference(self):
+        return self.y
 
-        def __set__(self, doc):
-            self.y = doc
-            self._cached_alignment = None
-            self._cached_words_y = [t.text for t in doc]
+    @reference.setter
+    def reference(self, doc):
+        self.y = doc
+        self._cached_alignment = None
+        self._cached_words_y = [t.text for t in doc]
 
     def copy(self):
         return Example(
@@ -420,9 +422,9 @@ cdef class Example:
                 seen_indices.update(indices)
         return output
 
-    property text:
-        def __get__(self):
-            return self.x.text
+    @property
+    def text(self):
+        return self.x.text
 
     def __str__(self):
         return str(self.to_dict())

spacy/vocab.pxd

@@ -41,7 +41,9 @@ cdef class Vocab:
     cdef const TokenC* make_fused_token(self, substrings) except NULL
 
     cdef const LexemeC* _new_lexeme(self, Pool mem, str string) except NULL
-    cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex) except -1
+    cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex, bint is_transient) except -1
     cdef const LexemeC* _new_lexeme(self, Pool mem, str string) except NULL
 
     cdef PreshMap _by_orth
+    cdef Pool _non_temp_mem
+    cdef vector[attr_t] _transient_orths

spacy/vocab.pyi

@@ -1,6 +1,8 @@
+from contextlib import contextmanager
 from pathlib import Path
 from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Union
 
+from cymem.cymem import Pool
 from thinc.types import Floats1d, FloatsXd
 
 from . import Language
@@ -67,6 +69,8 @@ class Vocab:
     def from_bytes(
         self, bytes_data: bytes, *, exclude: Iterable[str] = ...
     ) -> Vocab: ...
+    @contextmanager
+    def memory_zone(self, mem: Optional[Pool] = None) -> Iterator[Pool]: ...
 
 def pickle_vocab(vocab: Vocab) -> Any: ...
 def unpickle_vocab(

spacy/vocab.pyx

@@ -1,4 +1,6 @@
 import functools
+from contextlib import ExitStack, contextmanager
+from typing import Iterator, Optional
 
 import numpy
 import srsly
@@ -87,17 +89,24 @@ cdef class Vocab:
         self.lookups = lookups
         self.writing_system = writing_system
         self.get_noun_chunks = get_noun_chunks
+        # During a memory_zone we replace our mem object with one
+        # that's passed to us. We keep a reference to our non-temporary
+        # memory here, in case we need to make an allocation we want to
+        # guarantee is not temporary. This is also how we check whether
+        # we're in a memory zone: we check whether self.mem is self._non_temp_mem
+        self._non_temp_mem = self.mem
 
-    property vectors:
-        def __get__(self):
-            return self._vectors
+    @property
+    def vectors(self):
+        return self._vectors
 
-        def __set__(self, vectors):
-            if hasattr(vectors, "strings"):
-                for s in vectors.strings:
-                    self.strings.add(s)
-            self._vectors = vectors
-            self._vectors.strings = self.strings
+    @vectors.setter
+    def vectors(self, vectors):
+        if hasattr(vectors, "strings"):
+            for s in vectors.strings:
+                self.strings.add(s)
+        self._vectors = vectors
+        self._vectors.strings = self.strings
 
     @property
     def lang(self):
@@ -113,6 +122,33 @@ cdef class Vocab:
         """
         return self.length
 
+    @contextmanager
+    def memory_zone(self, mem: Optional[Pool] = None) -> Iterator[Pool]:
+        """Begin a block where resources allocated during the block will
+        be freed at the end of it. If a resources was created within the
+        memory zone block, accessing it outside the block is invalid.
+        Behaviour of this invalid access is undefined. Memory zones should
+        not be nested.
+
+        The memory zone is helpful for services that need to process large
+        volumes of text with a defined memory budget.
+        """
+        if mem is None:
+            mem = Pool()
+        # The ExitStack allows programmatic nested context managers.
+        # We don't know how many we need, so it would be awkward to have
+        # them as nested blocks.
+        with ExitStack() as stack:
+            contexts = [stack.enter_context(self.strings.memory_zone(mem))]
+            if hasattr(self.morphology, "memory_zone"):
+                contexts.append(stack.enter_context(self.morphology.memory_zone(mem)))
+            if hasattr(self._vectors, "memory_zone"):
+                contexts.append(stack.enter_context(self._vectors.memory_zone(mem)))
+            self.mem = mem
+            yield mem
+        self._clear_transient_orths()
+        self.mem = self._non_temp_mem
+
     def add_flag(self, flag_getter, int flag_id=-1):
         """Set a new boolean flag to words in the vocabulary.
 
@@ -147,8 +183,7 @@ cdef class Vocab:
 
     cdef const LexemeC* get(self, Pool mem, str string) except NULL:
         """Get a pointer to a `LexemeC` from the lexicon, creating a new
-        `Lexeme` if necessary using memory acquired from the given pool. If the
-        pool is the lexicon's own memory, the lexeme is saved in the lexicon.
+        `Lexeme` if necessary.
         """
         if string == "":
             return &EMPTY_LEXEME
@@ -179,17 +214,9 @@ cdef class Vocab:
             return self._new_lexeme(mem, self.strings[orth])
 
     cdef const LexemeC* _new_lexeme(self, Pool mem, str string) except NULL:
-        # I think this heuristic is bad, and the Vocab should always
-        # own the lexemes. It avoids weird bugs this way, as it's how the thing
-        # was originally supposed to work. The best solution to the growing
-        # memory use is to periodically reset the vocab, which is an action
-        # that should be up to the user to do (so we don't need to keep track
-        # of the doc ownership).
-        # TODO: Change the C API so that the mem isn't passed in here.
+        # The mem argument is deprecated, replaced by memory zones. Same with
+        # this size heuristic.
         mem = self.mem
-        # if len(string) < 3 or self.length < 10000:
-        #    mem = self.mem
-        cdef bint is_oov = mem is not self.mem
         lex = <LexemeC*>mem.alloc(1, sizeof(LexemeC))
         lex.orth = self.strings.add(string)
         lex.length = len(string)
@@ -201,18 +228,25 @@ cdef class Vocab:
             for attr, func in self.lex_attr_getters.items():
                 value = func(string)
                 if isinstance(value, str):
-                    value = self.strings.add(value)
+                    value = self.strings.add(value, allow_transient=True)
                 if value is not None:
                     Lexeme.set_struct_attr(lex, attr, value)
-        if not is_oov:
-            self._add_lex_to_vocab(lex.orth, lex)
+        self._add_lex_to_vocab(lex.orth, lex, self.mem is not self._non_temp_mem)
         if lex == NULL:
             raise ValueError(Errors.E085.format(string=string))
         return lex
 
-    cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex) except -1:
+    cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex, bint is_transient) except -1:
         self._by_orth.set(lex.orth, <void*>lex)
         self.length += 1
+        if is_transient:
+            self._transient_orths.push_back(lex.orth)
+
+    def _clear_transient_orths(self):
+        """Remove transient lexemes from the index (generally at the end of the memory zone)"""
+        for orth in self._transient_orths:
+            self._by_orth.pop(orth)
+        self._transient_orths.clear()
 
     def __contains__(self, key):
         """Check whether the string or int key has an entry in the vocabulary.
@@ -264,7 +298,7 @@ cdef class Vocab:
         """
         cdef attr_t orth
         if isinstance(id_or_string, str):
-            orth = self.strings.add(id_or_string)
+            orth = self.strings.add(id_or_string, allow_transient=True)
         else:
             orth = id_or_string
         return Lexeme(self, orth)
@@ -416,7 +450,7 @@ cdef class Vocab:
         DOCS: https://spacy.io/api/vocab#get_vector
         """
         if isinstance(orth, str):
-            orth = self.strings.add(orth)
+            orth = self.strings.add(orth, allow_transient=True)
         cdef Lexeme lex = self[orth]
         key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
         if self.has_vector(key):
@@ -435,7 +469,7 @@ cdef class Vocab:
         DOCS: https://spacy.io/api/vocab#set_vector
         """
         if isinstance(orth, str):
-            orth = self.strings.add(orth)
+            orth = self.strings.add(orth, allow_transient=False)
         cdef Lexeme lex = self[orth]
         key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
         if self.vectors.is_full and key not in self.vectors:
@@ -459,22 +493,23 @@ cdef class Vocab:
         DOCS: https://spacy.io/api/vocab#has_vector
         """
         if isinstance(orth, str):
-            orth = self.strings.add(orth)
+            orth = self.strings.add(orth, allow_transient=True)
         cdef Lexeme lex = self[orth]
         key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
         return key in self.vectors
 
-    property lookups:
-        def __get__(self):
-            return self._lookups
+    @property
+    def lookups(self):
+        return self._lookups
 
-        def __set__(self, lookups):
-            self._lookups = lookups
-            if lookups.has_table("lexeme_norm"):
-                self.lex_attr_getters[NORM] = util.add_lookups(
-                    self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]),
-                    self.lookups.get_table("lexeme_norm"),
-                )
+    @lookups.setter
+    def lookups(self, lookups):
+        self._lookups = lookups
+        if lookups.has_table("lexeme_norm"):
+            self.lex_attr_getters[NORM] = util.add_lookups(
+                self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]),
+                self.lookups.get_table("lexeme_norm"),
+            )
 
     def to_disk(self, path, *, exclude=tuple()):
         """Save the current state to a directory.

website/docs/api/attributes.mdx

@@ -45,33 +45,33 @@ For attributes that represent string values, the internal integer ID is accessed
 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_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~~                                                                                                        |
-| `IS_ASCII`   | Token text consists of ASCII characters. ~~bool~~                                                                                                             |
-| `IS_DIGIT`   | Token text consists of digits. ~~bool~~                                                                                                                       |
-| `IS_LOWER`   | Token text is in lowercase. ~~bool~~                                                                                                                          |
-| `IS_PUNCT`   | Token is punctuation. ~~bool~~                                                                                                                                |
-| `IS_SPACE`   | Token is whitespace. ~~bool~~                                                                                                                                 |
-| `IS_STOP`    | Token is a stop word. ~~bool~~                                                                                                                                |
-| `IS_TITLE`   | Token text is in titlecase. ~~bool~~                                                                                                                          |
-| `IS_UPPER`   | Token text is in uppercase. ~~bool~~                                                                                                                          |
-| `LEMMA`      | The token's lemma. ~~str~~                                                                                                                                    |
-| `LENGTH`     | The length of the token text. ~~int~~                                                                                                                         |
-| `LIKE_EMAIL` | Token text resembles an email address. ~~bool~~                                                                                                               |
-| `LIKE_NUM`   | Token text resembles a number. ~~bool~~                                                                                                                       |
-| `LIKE_URL`   | Token text resembles a URL. ~~bool~~                                                                                                                          |
-| `LOWER`      | The lowercase form of the token text. ~~str~~                                                                                                                 |
-| `MORPH`      | The token's morphological analysis. ~~MorphAnalysis~~                                                                                                         |
-| `NORM`       | The normalized form of the token text. ~~str~~                                                                                                                |
-| `ORTH`       | The exact verbatim text of a token. ~~str~~                                                                                                                   |
-| `POS`        | The token's universal part of speech (UPOS). ~~str~~                                                                                                          |
-| `SENT_START` | Token is start of sentence. ~~bool~~                                                                                                                          |
-| `SHAPE`      | The token's shape. ~~str~~                                                                                                                                    |
-| `SPACY`      | Token has a trailing space. ~~bool~~                                                                                                                          |
-| `TAG`        | The token's fine-grained part of speech. ~~str~~                                                                                                              |
+| 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 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~~                                                                                                         |
+| `IS_ASCII`   | Token text consists of ASCII characters. ~~bool~~                                                                                                              |
+| `IS_DIGIT`   | Token text consists of digits. ~~bool~~                                                                                                                        |
+| `IS_LOWER`   | Token text is in lowercase. ~~bool~~                                                                                                                           |
+| `IS_PUNCT`   | Token is punctuation. ~~bool~~                                                                                                                                 |
+| `IS_SPACE`   | Token is whitespace. ~~bool~~                                                                                                                                  |
+| `IS_STOP`    | Token is a stop word. ~~bool~~                                                                                                                                 |
+| `IS_TITLE`   | Token text is in titlecase. ~~bool~~                                                                                                                           |
+| `IS_UPPER`   | Token text is in uppercase. ~~bool~~                                                                                                                           |
+| `LEMMA`      | The token's lemma. ~~str~~                                                                                                                                     |
+| `LENGTH`     | The length of the token text. ~~int~~                                                                                                                          |
+| `LIKE_EMAIL` | Token text resembles an email address. ~~bool~~                                                                                                                |
+| `LIKE_NUM`   | Token text resembles a number. ~~bool~~                                                                                                                        |
+| `LIKE_URL`   | Token text resembles a URL. ~~bool~~                                                                                                                           |
+| `LOWER`      | The lowercase form of the token text. ~~str~~                                                                                                                  |
+| `MORPH`      | The token's morphological analysis. ~~MorphAnalysis~~                                                                                                          |
+| `NORM`       | The normalized form of the token text. ~~str~~                                                                                                                 |
+| `ORTH`       | The exact verbatim text of a token. ~~str~~                                                                                                                    |
+| `POS`        | The token's universal part of speech (UPOS). ~~str~~                                                                                                           |
+| `SENT_START` | Token is start of sentence. ~~bool~~                                                                                                                           |
+| `SHAPE`      | The token's shape. ~~str~~                                                                                                                                     |
+| `SPACY`      | Token has a trailing space. ~~bool~~                                                                                                                           |
+| `TAG`        | The token's fine-grained part of speech. ~~str~~                                                                                                               |

website/docs/api/cli.mdx

@@ -567,7 +567,7 @@ 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 ===========================
@@ -1320,7 +1320,7 @@ $ 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 or not to include the local context 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 to copy entities from the gold docs or not. 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 to copy entities from the gold docs or not. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~bool~~                                                                                                                            |
 | `get_candidates`                                    | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~                                         |
 | `get_candidates_batch` <Tag variant="new">3.5</Tag> | Function that generates plausible candidates for a given batch of `Span` objects. Defaults to [CandidateBatchGenerator](/api/architectures#CandidateBatchGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]]~~ |
 | `generate_empty_kb` <Tag variant="new">3.5.1</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]~~                                                                                                                                                                                                          |
-| `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]~~                     |
 
 ```python
 %%GITHUB_SPACY/spacy/pipeline/entity_linker.py
@@ -100,21 +100,21 @@ custom knowledge base, you should either call
 [`set_kb`](/api/entitylinker#set_kb) or provide a `kb_loader` in the
 [`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~~                                                                                                                                                                                         |
-| _keyword-only_                           |                                                                                                                                                                                                                                                                                             |
-| `entity_vector_length`                   | Size of encoding vectors in the KB. ~~int~~                                                                                                                                                                                                                                                 |
-| `get_candidates`                         | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~                                                                                                                                                            |
-| `labels_discard`                         | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~                                                                                                                                                                                                              |
-| `n_sents`                                | The number of neighbouring sentences to take into account. ~~int~~                                                                                                                                                                                                                          |
-| `incl_prior`                             | Whether or not to include prior probabilities from the KB in the model. ~~bool~~                                                                                                                                                                                                            |
-| `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]~~ |
+| 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~~                                                                                                                                                                                          |
+| _keyword-only_                           |                                                                                                                                                                                                                                                                                              |
+| `entity_vector_length`                   | Size of encoding vectors in the KB. ~~int~~                                                                                                                                                                                                                                                  |
+| `get_candidates`                         | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~                                                                                                                                                             |
+| `labels_discard`                         | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~                                                                                                                                                                                                               |
+| `n_sents`                                | The number of neighbouring sentences to take into account. ~~int~~                                                                                                                                                                                                                           |
+| `incl_prior`                             | Whether or not to include prior probabilities from the KB in the model. ~~bool~~                                                                                                                                                                                                             |
+| `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 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/entityruler.mdx

@@ -58,7 +58,7 @@ how the component should be configured. You can override its settings via the
 | Setting                                              | Description                                                                                                                                                                                   |
 | ---------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `phrase_matcher_attr`                                | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. Defaults to `None`. ~~Optional[Union[int, str]]~~ |
-| `matcher_fuzzy_compare` <Tag variant="new">3.5</Tag> | The fuzzy comparison method, passed on to the internal `Matcher`. Defaults to `spacy.matcher.levenshtein.levenshtein_compare`. ~~Callable~~                                                             |
+| `matcher_fuzzy_compare` <Tag variant="new">3.5</Tag> | The fuzzy comparison method, passed on to the internal `Matcher`. Defaults to `spacy.matcher.levenshtein.levenshtein_compare`. ~~Callable~~                                                   |
 | `validate`                                           | Whether patterns should be validated (passed to the `Matcher` and `PhraseMatcher`). Defaults to `False`. ~~bool~~                                                                             |
 | `overwrite_ents`                                     | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. ~~bool~~                                                     |
 | `ent_id_sep`                                         | Separator used internally for entity IDs. Defaults to `"\|\|"`. ~~str~~                                                                                                                       |
@@ -92,7 +92,7 @@ be a token pattern (list) or a phrase pattern (string). For example:
 | `name` <Tag variant="new">3</Tag>                    | Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object. ~~str~~ |
 | _keyword-only_                                       |                                                                                                                                                                                                                                       |
 | `phrase_matcher_attr`                                | Optional attribute name match on for the internal [`PhraseMatcher`](/api/phrasematcher), e.g. `LOWER` to match on the lowercase token text. Defaults to `None`. ~~Optional[Union[int, str]]~~                                         |
-| `matcher_fuzzy_compare` <Tag variant="new">3.5</Tag> | The fuzzy comparison method, passed on to the internal `Matcher`. Defaults to `spacy.matcher.levenshtein.levenshtein_compare`. ~~Callable~~                                                                                                     |
+| `matcher_fuzzy_compare` <Tag variant="new">3.5</Tag> | The fuzzy comparison method, passed on to the internal `Matcher`. Defaults to `spacy.matcher.levenshtein.levenshtein_compare`. ~~Callable~~                                                                                           |
 | `validate`                                           | Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate`. Defaults to `False`. ~~bool~~                                                                                                                |
 | `overwrite_ents`                                     | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. ~~bool~~                                                                                             |
 | `ent_id_sep`                                         | Separator used internally for entity IDs. Defaults to `"\|\|"`. ~~str~~                                                                                                                                                               |
@@ -173,7 +173,7 @@ happens automatically after the component has been added to the pipeline using
 [`nlp.add_pipe`](/api/language#add_pipe). If the entity ruler was initialized
 with `overwrite_ents=True`, existing entities will be replaced if they overlap
 with the matches. When matches overlap in a Doc, the entity ruler prioritizes
-longer patterns over shorter, and if equal the match occuring first in the Doc
+longer patterns over shorter, and if equal the match occurring first in the Doc
 is chosen.
 
 > #### Example

website/docs/api/morphology.mdx

@@ -147,9 +147,10 @@ Whether a feature/value pair is in the analysis.
 > assert "Feat1=Val1" in morph
 > ```
 
-| Name        | Description                                   |
-| ----------- | --------------------------------------------- |
-| **RETURNS** | A feature/value pair in the analysis. ~~str~~ |
+| Name         | Description                                                           |
+| ------------ | --------------------------------------------------------------------- |
+| `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

@@ -288,7 +288,7 @@ 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

@@ -526,13 +526,17 @@ 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
-snake when it's called:
+[pipeline component](/usage/processing-pipelines#custom-components) that prints
+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%

website/src/templates/index.js

@@ -58,8 +58,8 @@ const AlertSpace = ({ nightly, legacy }) => {
 }
 
 const navAlert = (
-    <Link to="https://form.typeform.com/to/WlflqP1b" noLinkLayout>
-        💥 Interested in <strong>Premium spaCy Models</strong>?
+    <Link to="https://explosion.ai/blog/sp-global-commodities" noLinkLayout>
+        💥 <strong>New:</strong> Case study with S&P Global
     </Link>
 )