Added Haitian Creole (ht) Language Support to spaCy (#13807)

This PR adds official support for Haitian Creole (ht) to spaCy's spacy/lang module.
It includes:

    Added all core language data files for spacy/lang/ht:
        tokenizer_exceptions.py
        punctuation.py
        lex_attrs.py
        syntax_iterators.py
        lemmatizer.py
        stop_words.py
        tag_map.py

    Unit tests for tokenizer and noun chunking (test_tokenizer.py, test_noun_chunking.py, etc.). Passed all 58 pytest spacy/tests/lang/ht tests that I've created.

    Basic tokenizer rules adapted for Haitian Creole orthography and informal contractions.

    Custom like_num atrribute supporting Haitian number formats (e.g., "3yèm").

    Support for common informal apostrophe usage (e.g., "m'ap", "n'ap", "di'm").

    Ensured no breakages in other language modules.

    Followed spaCy coding style (PEP8, Black).

This provides a foundation for Haitian Creole NLP development using spaCy.

.github/FUNDING.yml

@@ -0,0 +1 @@
+custom: [https://explosion.ai/merch, https://explosion.ai/tailored-solutions]

.github/workflows/cibuildwheel.yml

@@ -0,0 +1,99 @@
+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: ${{ matrix.os }}
+    strategy:
+      matrix:
+        # macos-13 is an intel runner, macos-14 is apple silicon
+        os: [ubuntu-latest, windows-latest, macos-13, macos-14, ubuntu-24.04-arm]
+
+    steps:
+      - uses: actions/checkout@v4
+      # aarch64 (arm) is built via qemu emulation
+      # QEMU is sadly too slow. We need to wait for public ARM support
+      #- name: Set up QEMU
+      #  if: runner.os == 'Linux'
+      #  uses: docker/setup-qemu-action@v3
+      #  with:
+      #    platforms: all
+      - name: Build wheels
+        uses: pypa/cibuildwheel@v2.21.3
+        env:
+          CIBW_ARCHS_LINUX: auto
+        with:
+          package-dir: .
+          output-dir: wheelhouse
+          config-file: "{package}/pyproject.toml"
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cibw-wheels-${{ 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

@@ -2,6 +2,8 @@ name: tests
 
 on:
   push:
+    tags-ignore:
+      - '**'
     branches-ignore:
       - "spacy.io"
       - "nightly.spacy.io"
@@ -10,7 +12,6 @@ on:
       - "*.md"
       - "*.mdx"
       - "website/**"
-      - ".github/workflows/**"
   pull_request:
     types: [opened, synchronize, reopened, edited]
     paths-ignore:
@@ -25,13 +26,12 @@ 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"
+          python-version: "3.10"
-          architecture: x64
 
       - name: black
         run: |
@@ -45,11 +45,12 @@ jobs:
         run: |
           python -m pip install flake8==5.0.4
           python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
-      - name: cython-lint
+          # Unfortunately cython-lint isn't working after the shift to Cython 3.
-        run: |
+          #- name: cython-lint
-          python -m pip install cython-lint -c requirements.txt
+          #  run: |
-          # E501: line too log, W291: trailing whitespace, E266: too many leading '#' for block comment
+          #    python -m pip install cython-lint -c requirements.txt
-          cython-lint spacy --ignore E501,W291,E266
+          #    # E501: line too log, W291: trailing whitespace, E266: too many leading '#' for block comment
+          #    cython-lint spacy --ignore E501,W291,E266
 
   tests:
     name: Test
@@ -58,28 +59,18 @@ jobs:
       fail-fast: true
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python_version: ["3.11", "3.12.0-rc.2"]
+        python_version: ["3.9", "3.12", "3.13"]
-        include:
-          - os: windows-latest
-            python_version: "3.7"
-          - os: macos-latest
-            python_version: "3.8"
-          - os: ubuntu-latest
-            python_version: "3.9"
-          - os: windows-latest
-            python_version: "3.10"
 
     runs-on: ${{ matrix.os }}
 
     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: |
@@ -157,7 +148,9 @@ jobs:
       - name: "Test assemble CLI"
         run: |
           python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
-          PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
+          python -m spacy assemble ner_source_sm.cfg output_dir
+        env:
+          PYTHONWARNINGS: "error,ignore::DeprecationWarning" 
         if: matrix.python_version == '3.9'
 
       - name: "Test assemble CLI vectors warning"

.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

@@ -35,7 +35,7 @@ so that more people can benefit from it.
 
 When opening an issue, use a **descriptive title** and include your
 **environment** (operating system, Python version, spaCy version). Our
-[issue template](https://github.com/explosion/spaCy/issues/new) helps you
+[issue templates](https://github.com/explosion/spaCy/issues/new/choose) help you
 remember the most important details to include. If you've discovered a bug, you
 can also submit a [regression test](#fixing-bugs) straight away. When you're
 opening an issue to report the bug, simply refer to your pull request in the
@@ -449,13 +449,12 @@ and plugins in spaCy v3.0, and we can't wait to see what you build with it!
   [`spacy`](https://github.com/topics/spacy?o=desc&s=stars) and
   [`spacy-extensions`](https://github.com/topics/spacy-extension?o=desc&s=stars)
   to make it easier to find. Those are also the topics we're linking to from the
-  spaCy website. If you're sharing your project on Twitter, feel free to tag
+  spaCy website. If you're sharing your project on X, feel free to tag
-  [@spacy_io](https://twitter.com/spacy_io) so we can check it out.
+  [@spacy_io](https://x.com/spacy_io) so we can check it out.
 
-- Once your extension is published, you can open an issue on the
+- Once your extension is published, you can open a
-  [issue tracker](https://github.com/explosion/spacy/issues) to suggest it for the
+  [PR](https://github.com/explosion/spaCy/pulls) to suggest it for the
-  [resources directory](https://spacy.io/usage/resources#extensions) on the
+  [Universe](https://spacy.io/universe) page.
-  website.
 
 📖 **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) 2016-2022 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

MANIFEST.in

@@ -4,5 +4,6 @@ include README.md
 include pyproject.toml
 include spacy/py.typed
 recursive-include spacy/cli *.yml
+recursive-include spacy/tests *.json
 recursive-include licenses *
 recursive-exclude spacy *.cpp

README.md

@@ -16,7 +16,7 @@ model packaging, deployment and workflow management. spaCy is commercial
 open-source software, released under the
 [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
 
-💫 **Version 3.7 out now!**
+💫 **Version 3.8 out now!**
 [Check out the release notes here.](https://github.com/explosion/spaCy/releases)
 
 [![tests](https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/spaCy/actions/workflows/tests.yml)
@@ -28,7 +28,6 @@ open-source software, released under the
 <br />
 [![PyPi downloads](https://static.pepy.tech/personalized-badge/spacy?period=total&units=international_system&left_color=grey&right_color=orange&left_text=pip%20downloads)](https://pypi.org/project/spacy/)
 [![Conda downloads](https://img.shields.io/conda/dn/conda-forge/spacy?label=conda%20downloads)](https://anaconda.org/conda-forge/spacy)
-[![spaCy on Twitter](https://img.shields.io/twitter/follow/spacy_io.svg?style=social&label=Follow)](https://twitter.com/spacy_io)
 
 ## 📖 Documentation
 
@@ -39,28 +38,37 @@ open-source software, released under the
 | 🚀 **[New in v3.0]**                                                                                                                                                                                                      | New features, backwards incompatibilities and migration guide.                                                                                                                                                                                                                                                                               |
 | 🪐 **[Project Templates]**                                                                                                                                                                                                | End-to-end workflows you can clone, modify and run.                                                                                                                                                                                                                                                                                          |
 | 🎛 **[API Reference]**                                                                                                                                                                                                     | The detailed reference for spaCy's API.                                                                                                                                                                                                                                                                                                      |
+| ⏩ **[GPU Processing]**                                                                                                                                                                                                    | Use spaCy with CUDA-compatible GPU processing.                                                                                                                                                                                                                                                                                               |
 | 📦 **[Models]**                                                                                                                                                                                                           | Download trained pipelines for spaCy.                                                                                                                                                                                                                                                                                                        |
+| 🦙 **[Large Language Models]**                                                                                                                                                                                            | Integrate LLMs into spaCy pipelines.                                                                                                                                                                                                                                                                                                        |
 | 🌌 **[Universe]**                                                                                                                                                                                                         | Plugins, extensions, demos and books from the spaCy ecosystem.                                                                                                                                                                                                                                                                               |
 | ⚙️ **[spaCy VS Code Extension]**                                                                                                                                                                                          | Additional tooling and features for working with spaCy's config files.                                                                                                                                                                                                                                                                       |
 | 👩‍🏫 **[Online Course]**                                                                                                                                                                                                    | Learn spaCy in this free and interactive online course.                                                                                                                                                                                                                                                                                      |
+| 📰 **[Blog]**                                                                                                                                                                                                             | Read about current spaCy and Prodigy development, releases, talks and more from Explosion.                                                                                                                                                                                                                 |
 | 📺 **[Videos]**                                                                                                                                                                                                           | Our YouTube channel with video tutorials, talks and more.                                                                                                                                                                                                                                                                                    |
+| 🔴 **[Live Stream]**                                                                                                                                                                                                       | Join Matt as he works on spaCy and chat about NLP, live every week.                                                                                                                                                                                                                                                                         |
 | 🛠 **[Changelog]**                                                                                                                                                                                                         | Changes and version history.                                                                                                                                                                                                                                                                                                                 |
 | 💝 **[Contribute]**                                                                                                                                                                                                       | How to contribute to the spaCy project and code base.                                                                                                                                                                                                                                                                                        |
-| <a href="https://explosion.ai/spacy-tailored-pipelines"><img src="https://user-images.githubusercontent.com/13643239/152853098-1c761611-ccb0-4ec6-9066-b234552831fe.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-pipelines)**                 |
+| 👕 **[Swag]**                                                                                                                                                                                                             | Support us and our work with unique, custom-designed swag!                                                                                                                                                                                                                                                                                   |
-| <a href="https://explosion.ai/spacy-tailored-analysis"><img src="https://user-images.githubusercontent.com/1019791/206151300-b00cd189-e503-4797-aa1e-1bb6344062c5.png" width="125" alt="spaCy Tailored Pipelines"/></a>   | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-analysis)** |
+| <a href="https://explosion.ai/tailored-solutions"><img src="https://github.com/explosion/spaCy/assets/13643239/36d2a42e-98c0-4599-90e1-788ef75181be" width="150" alt="Tailored Solutions"/></a> | Custom NLP consulting, implementation and strategic advice by spaCy’s core development team. Streamlined, production-ready, predictable and maintainable. Send us an email or take our 5-minute questionnaire, and well'be in touch! **[Learn more &rarr;](https://explosion.ai/tailored-solutions)**                 |
 
 [spacy 101]: https://spacy.io/usage/spacy-101
 [new in v3.0]: https://spacy.io/usage/v3
 [usage guides]: https://spacy.io/usage/
 [api reference]: https://spacy.io/api/
+[gpu processing]: https://spacy.io/usage#gpu
 [models]: https://spacy.io/models
+[large language models]: https://spacy.io/usage/large-language-models
 [universe]: https://spacy.io/universe
 [spacy vs code extension]: https://github.com/explosion/spacy-vscode
 [videos]: https://www.youtube.com/c/ExplosionAI
+[live stream]: https://www.youtube.com/playlist?list=PLBmcuObd5An5_iAxNYLJa_xWmNzsYce8c
 [online course]: https://course.spacy.io
+[blog]: https://explosion.ai
 [project templates]: https://github.com/explosion/projects
 [changelog]: https://spacy.io/usage#changelog
 [contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
+[swag]: https://explosion.ai/merch
 
 ## 💬 Where to ask questions
 
@@ -72,13 +80,14 @@ more people can benefit from it.
 | Type                            | Platforms                               |
 | ------------------------------- | --------------------------------------- |
 | 🚨 **Bug Reports**              | [GitHub Issue Tracker]                  |
-| 🎁 **Feature Requests & Ideas** | [GitHub Discussions]                    |
+| 🎁 **Feature Requests & Ideas** | [GitHub Discussions] · [Live Stream]    |
 | 👩‍💻 **Usage Questions**          | [GitHub Discussions] · [Stack Overflow] |
-| 🗯 **General Discussion**        | [GitHub Discussions]                    |
+| 🗯 **General Discussion**        | [GitHub Discussions] · [Live Stream]   |
 
 [github issue tracker]: https://github.com/explosion/spaCy/issues
 [github discussions]: https://github.com/explosion/spaCy/discussions
 [stack overflow]: https://stackoverflow.com/questions/tagged/spacy
+[live stream]: https://www.youtube.com/playlist?list=PLBmcuObd5An5_iAxNYLJa_xWmNzsYce8c
 
 ## Features
 
@@ -108,7 +117,7 @@ For detailed installation instructions, see the
 
 - **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
   Studio)
-- **Python version**: Python 3.7+ (only 64 bit)
+- **Python version**: Python >=3.7, <3.13 (only 64 bit)
 - **Package managers**: [pip] · [conda] (via `conda-forge`)
 
 [pip]: https://pypi.org/project/spacy/

bin/release.sh

@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+
+set -e
+
+# Insist repository is clean
+git diff-index --quiet HEAD
+
+version=$(grep "__version__ = " spacy/about.py)
+version=${version/__version__ = }
+version=${version/\'/}
+version=${version/\'/}
+version=${version/\"/}
+version=${version/\"/}
+
+echo "Pushing release-v"$version
+
+git tag -d release-v$version || true
+git push origin :release-v$version || true
+git tag release-v$version
+git push origin release-v$version

build-constraints.txt

@@ -1,6 +1,2 @@
 # build version constraints for use with wheelwright
-numpy==1.15.0; python_version=='3.7' and platform_machine!='aarch64'
+numpy>=2.0.0,<3.0.0
-numpy==1.19.2; python_version=='3.7' and platform_machine=='aarch64'
-numpy==1.17.3; python_version=='3.8' and platform_machine!='aarch64'
-numpy==1.19.2; python_version=='3.8' and platform_machine=='aarch64'
-numpy>=1.25.0; python_version>='3.9'

licenses/3rd_party_licenses.txt

@@ -158,3 +158,45 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
+
+
+SciPy
+-----
+
+* Files: scorer.py
+
+The implementation of trapezoid() is adapted from SciPy, which is distributed
+under the following license:
+
+New BSD License
+
+Copyright (c) 2001-2002 Enthought, Inc. 2003-2023, SciPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above
+   copyright notice, this list of conditions and the following
+   disclaimer in the documentation and/or other materials provided
+   with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

pyproject.toml

@@ -1,15 +1,67 @@
 [build-system]
 requires = [
     "setuptools",
-    "cython>=0.25,<3.0",
+    "cython>=3.0,<4.0",
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.1.8,<8.3.0",
+    "thinc>=8.3.4,<8.4.0",
-    "numpy>=1.15.0; python_version < '3.9'",
+    "numpy>=2.0.0,<3.0.0"
-    "numpy>=1.25.0; python_version >= '3.9'",
 ]
 build-backend = "setuptools.build_meta"
 
+[tool.cibuildwheel]
+build = "*"
+skip = "pp* cp36* cp37* cp38* *-win32 *i686*"
+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

@@ -3,30 +3,26 @@ spacy-legacy>=3.0.11,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.1.8,<8.3.0
+thinc>=8.3.4,<8.4.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0
 srsly>=2.4.3,<3.0.0
 catalogue>=2.0.6,<2.1.0
-typer>=0.3.0,<0.10.0
+typer-slim>=0.3.0,<1.0.0
-smart-open>=5.2.1,<7.0.0
+weasel>=0.1.0,<0.5.0
-weasel>=0.1.0,<0.4.0
 # Third party dependencies
-numpy>=1.15.0; python_version < "3.9"
+numpy>=2.0.0,<3.0.0
-numpy>=1.19.0; python_version >= "3.9"
 requests>=2.13.0,<3.0.0
 tqdm>=4.38.0,<5.0.0
 pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0
 jinja2
-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
+cython>=3.0,<4.0
 pytest>=5.2.0,!=7.1.0
 pytest-timeout>=1.3.0,<2.0.0
 mock>=2.0.0,<3.0.0

setup.cfg

@@ -17,11 +17,11 @@ classifiers =
     Operating System :: Microsoft :: Windows
     Programming Language :: Cython
     Programming Language :: Python :: 3
-    Programming Language :: Python :: 3.7
-    Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
     Programming Language :: Python :: 3.11
+    Programming Language :: Python :: 3.12
+    Programming Language :: Python :: 3.13
     Topic :: Scientific/Engineering
 project_urls =
     Release notes = https://github.com/explosion/spaCy/releases
@@ -30,18 +30,18 @@ project_urls =
 [options]
 zip_safe = false
 include_package_data = true
-python_requires = >=3.7
+python_requires = >=3.9,<3.14
 # NOTE: This section is superseded by pyproject.toml and will be removed in
 # spaCy v4
 setup_requires =
-    cython>=0.25,<3.0
+    cython>=3.0,<4.0
-    numpy>=1.15.0; python_version < "3.9"
+    numpy>=2.0.0,<3.0.0; python_version < "3.9"
-    numpy>=1.19.0; python_version >= "3.9"
+    numpy>=2.0.0,<3.0.0; python_version >= "3.9"
     # We also need our Cython packages here to compile against
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.1.8,<8.3.0
+    thinc>=8.3.4,<8.4.0
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.11,<3.1.0
@@ -49,14 +49,13 @@ install_requires =
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.1.8,<8.3.0
+    thinc>=8.3.4,<8.4.0
     wasabi>=0.9.1,<1.2.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0
-    weasel>=0.1.0,<0.4.0
+    weasel>=0.1.0,<0.5.0
     # Third-party dependencies
-    typer>=0.3.0,<0.10.0
+    typer-slim>=0.3.0,<1.0.0
-    smart-open>=5.2.1,<7.0.0
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0; python_version < "3.9"
     numpy>=1.19.0; python_version >= "3.9"
@@ -66,8 +65,6 @@ 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]
 console_scripts =
@@ -117,7 +114,7 @@ cuda12x =
 cuda-autodetect =
     cupy-wheel>=11.0.0,<13.0.0
 apple =
-    thinc-apple-ops>=0.1.0.dev0,<1.0.0
+    thinc-apple-ops>=1.0.0,<2.0.0
 # Language tokenizers with external dependencies
 ja =
     sudachipy>=0.5.2,!=0.6.1

spacy/__init__.py

@@ -17,6 +17,7 @@ from .cli.info import info  # noqa: F401
 from .errors import Errors
 from .glossary import explain  # noqa: F401
 from .language import Language
+from .registrations import REGISTRY_POPULATED, populate_registry
 from .util import logger, registry  # noqa: F401
 from .vocab import Vocab
 

spacy/about.py

@@ -1,5 +1,5 @@
 # fmt: off
 __title__ = "spacy"
-__version__ = "3.7.2"
+__version__ = "3.8.7"
 __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/benchmark_speed.py

@@ -13,7 +13,7 @@ from .. import util
 from ..language import Language
 from ..tokens import Doc
 from ..training import Corpus
-from ._util import Arg, Opt, benchmark_cli, setup_gpu
+from ._util import Arg, Opt, benchmark_cli, import_code, setup_gpu
 
 
 @benchmark_cli.command(
@@ -30,12 +30,14 @@ def benchmark_speed_cli(
     use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
     n_batches: int = Opt(50, "--batches", help="Minimum number of batches to benchmark", min=30,),
     warmup_epochs: int = Opt(3, "--warmup", "-w", min=0, help="Number of iterations over the data for warmup"),
+    code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
     # fmt: on
 ):
     """
     Benchmark a pipeline. Expects a loadable spaCy pipeline and benchmark
     data in the binary .spacy format.
     """
+    import_code(code_path)
     setup_gpu(use_gpu=use_gpu, silent=False)
 
     nlp = util.load_model(model)
@@ -171,5 +173,5 @@ def print_outliers(sample: numpy.ndarray):
 def warmup(
     nlp: Language, docs: List[Doc], warmup_epochs: int, batch_size: Optional[int]
 ) -> numpy.ndarray:
-    docs = warmup_epochs * docs
+    docs = [doc.copy() for doc in docs * warmup_epochs]
     return annotate(nlp, docs, batch_size)

spacy/cli/debug_model.py

@@ -170,7 +170,7 @@ def debug_model(
         msg.divider(f"STEP 3 - prediction")
         msg.info(str(prediction))
 
-    msg.good(f"Succesfully ended analysis - model looks good.")
+    msg.good(f"Successfully ended analysis - model looks good.")
 
 
 def _sentences():

spacy/cli/download.py

@@ -1,5 +1,6 @@
 import sys
 from typing import Optional, Sequence
+from urllib.parse import urljoin
 
 import requests
 import typer
@@ -7,7 +8,14 @@ from wasabi import msg
 
 from .. import about
 from ..errors import OLD_MODEL_SHORTCUTS
-from ..util import get_minor_version, is_package, is_prerelease_version, run_command
+from ..util import (
+    get_minor_version,
+    is_in_interactive,
+    is_in_jupyter,
+    is_package,
+    is_prerelease_version,
+    run_command,
+)
 from ._util import SDIST_SUFFIX, WHEEL_SUFFIX, Arg, Opt, app
 
 
@@ -56,6 +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]
@@ -77,6 +92,27 @@ def download(
         "Download and installation successful",
         f"You can now load the package via spacy.load('{model_name}')",
     )
+    if is_in_jupyter():
+        reload_deps_msg = (
+            "If you are in a Jupyter or Colab notebook, you may need to "
+            "restart Python in order to load all the package's dependencies. "
+            "You can do this by selecting the 'Restart kernel' or 'Restart "
+            "runtime' option."
+        )
+        msg.warn(
+            "Restart to reload dependencies",
+            reload_deps_msg,
+        )
+    elif is_in_interactive():
+        reload_deps_msg = (
+            "If you are in an interactive Python session, you may need to "
+            "exit and restart Python to load all the package's dependencies. "
+            "You can exit with Ctrl-D (or Ctrl-Z and Enter on Windows)."
+        )
+        msg.warn(
+            "Restart to reload dependencies",
+            reload_deps_msg,
+        )
 
 
 def get_model_filename(model_name: str, version: str, sdist: bool = False) -> str:
@@ -125,7 +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/cli/package.py

@@ -1,5 +1,7 @@
+import os
 import re
 import shutil
+import subprocess
 import sys
 from collections import defaultdict
 from pathlib import Path
@@ -11,6 +13,7 @@ from thinc.api import Config
 from wasabi import MarkdownRenderer, Printer, get_raw_input
 
 from .. import about, util
+from ..compat import importlib_metadata
 from ..schemas import ModelMetaSchema, validate
 from ._util import SDIST_SUFFIX, WHEEL_SUFFIX, Arg, Opt, app, string_to_list
 
@@ -27,6 +30,7 @@ def package_cli(
     version: Optional[str] = Opt(None, "--version", "-v", help="Package version to override meta"),
     build: str = Opt("sdist", "--build", "-b", help="Comma-separated formats to build: sdist and/or wheel, or none."),
     force: bool = Opt(False, "--force", "-f", "-F", help="Force overwriting existing data in output directory"),
+    require_parent: bool = Opt(True, "--require-parent/--no-require-parent", "-R", "-R", help="Include the parent package (e.g. spacy) in the requirements"),
     # fmt: on
 ):
     """
@@ -35,7 +39,7 @@ def package_cli(
     specified output directory, and the data will be copied over. If
     --create-meta is set and a meta.json already exists in the output directory,
     the existing values will be used as the defaults in the command-line prompt.
-    After packaging, "python setup.py sdist" is run in the package directory,
+    After packaging, "python -m build --sdist" is run in the package directory,
     which will create a .tar.gz archive that can be installed via "pip install".
 
     If additional code files are provided (e.g. Python files containing custom
@@ -57,6 +61,7 @@ def package_cli(
         create_sdist=create_sdist,
         create_wheel=create_wheel,
         force=force,
+        require_parent=require_parent,
         silent=False,
     )
 
@@ -71,6 +76,7 @@ def package(
     create_meta: bool = False,
     create_sdist: bool = True,
     create_wheel: bool = False,
+    require_parent: bool = False,
     force: bool = False,
     silent: bool = True,
 ) -> None:
@@ -78,9 +84,17 @@ def package(
     input_path = util.ensure_path(input_dir)
     output_path = util.ensure_path(output_dir)
     meta_path = util.ensure_path(meta_path)
-    if create_wheel and not has_wheel():
+    if create_wheel and not has_wheel() and not has_build():
-        err = "Generating a binary .whl file requires wheel to be installed"
+        err = (
-        msg.fail(err, "pip install wheel", exits=1)
+            "Generating wheels requires 'build' or 'wheel' (deprecated) to be installed"
+        )
+        msg.fail(err, "pip install build", exits=1)
+    if not has_build():
+        msg.warn(
+            "Generating packages without the 'build' package is deprecated and "
+            "will not be supported in the future. To install 'build': pip "
+            "install build"
+        )
     if not input_path or not input_path.exists():
         msg.fail("Can't locate pipeline data", input_path, exits=1)
     if not output_path or not output_path.exists():
@@ -102,7 +116,7 @@ def package(
     if not meta_path.exists() or not meta_path.is_file():
         msg.fail("Can't load pipeline meta.json", meta_path, exits=1)
     meta = srsly.read_json(meta_path)
-    meta = get_meta(input_dir, meta)
+    meta = get_meta(input_dir, meta, require_parent=require_parent)
     if meta["requirements"]:
         msg.good(
             f"Including {len(meta['requirements'])} package requirement(s) from "
@@ -175,6 +189,7 @@ def package(
         imports.append(code_path.stem)
         shutil.copy(str(code_path), str(package_path))
     create_file(main_path / "meta.json", srsly.json_dumps(meta, indent=2))
+
     create_file(main_path / "setup.py", TEMPLATE_SETUP)
     create_file(main_path / "MANIFEST.in", TEMPLATE_MANIFEST)
     init_py = TEMPLATE_INIT.format(
@@ -184,12 +199,37 @@ def package(
     msg.good(f"Successfully created package directory '{model_name_v}'", main_path)
     if create_sdist:
         with util.working_dir(main_path):
-            util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
+            # run directly, since util.run_command is not designed to continue
+            # after a command fails
+            ret = subprocess.run(
+                [sys.executable, "-m", "build", ".", "--sdist"],
+                env=os.environ.copy(),
+            )
+            if ret.returncode != 0:
+                msg.warn(
+                    "Creating sdist with 'python -m build' failed. Falling "
+                    "back to deprecated use of 'python setup.py sdist'"
+                )
+                util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
         zip_file = main_path / "dist" / f"{model_name_v}{SDIST_SUFFIX}"
         msg.good(f"Successfully created zipped Python package", zip_file)
     if create_wheel:
         with util.working_dir(main_path):
-            util.run_command([sys.executable, "setup.py", "bdist_wheel"], capture=False)
+            # run directly, since util.run_command is not designed to continue
+            # after a command fails
+            ret = subprocess.run(
+                [sys.executable, "-m", "build", ".", "--wheel"],
+                env=os.environ.copy(),
+            )
+            if ret.returncode != 0:
+                msg.warn(
+                    "Creating wheel with 'python -m build' failed. Falling "
+                    "back to deprecated use of 'wheel' with "
+                    "'python setup.py bdist_wheel'"
+                )
+                util.run_command(
+                    [sys.executable, "setup.py", "bdist_wheel"], capture=False
+                )
         wheel_name_squashed = re.sub("_+", "_", model_name_v)
         wheel = main_path / "dist" / f"{wheel_name_squashed}{WHEEL_SUFFIX}"
         msg.good(f"Successfully created binary wheel", wheel)
@@ -209,6 +249,17 @@ def has_wheel() -> bool:
         return False
 
 
+def has_build() -> bool:
+    # it's very likely that there is a local directory named build/ (especially
+    # in an editable install), so an import check is not sufficient; instead
+    # check that there is a package version
+    try:
+        importlib_metadata.version("build")
+        return True
+    except importlib_metadata.PackageNotFoundError:  # type: ignore[attr-defined]
+        return False
+
+
 def get_third_party_dependencies(
     config: Config, exclude: List[str] = util.SimpleFrozenList()
 ) -> List[str]:
@@ -255,6 +306,8 @@ def get_third_party_dependencies(
                 modules.add(func_info["module"].split(".")[0])  # type: ignore[union-attr]
     dependencies = []
     for module_name in modules:
+        if module_name == about.__title__:
+            continue
         if module_name in distributions:
             dist = distributions.get(module_name)
             if dist:
@@ -285,7 +338,9 @@ def create_file(file_path: Path, contents: str) -> None:
 
 
 def get_meta(
-    model_path: Union[str, Path], existing_meta: Dict[str, Any]
+    model_path: Union[str, Path],
+    existing_meta: Dict[str, Any],
+    require_parent: bool = False,
 ) -> Dict[str, Any]:
     meta: Dict[str, Any] = {
         "lang": "en",
@@ -314,6 +369,8 @@ def get_meta(
     existing_reqs = [util.split_requirement(req)[0] for req in meta["requirements"]]
     reqs = get_third_party_dependencies(nlp.config, exclude=existing_reqs)
     meta["requirements"].extend(reqs)
+    if require_parent and about.__title__ not in meta["requirements"]:
+        meta["requirements"].append(about.__title__ + meta["spacy_version"])
     return meta
 
 
@@ -488,8 +545,11 @@ def list_files(data_dir):
 
 
 def list_requirements(meta):
-    parent_package = meta.get('parent_package', 'spacy')
+    # Up to version 3.7, we included the parent package
-    requirements = [parent_package + meta['spacy_version']]
+    # in requirements by default. This behaviour is removed
+    # in 3.8, with a setting to include the parent package in
+    # the requirements list in the meta if desired.
+    requirements = []
     if 'setup_requires' in meta:
         requirements += meta['setup_requires']
     if 'requirements' in meta:

spacy/cli/templates/quickstart_training.jinja

@@ -271,8 +271,9 @@ grad_factor = 1.0
 @layers = "reduce_mean.v1"
 
 [components.textcat.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
@@ -308,8 +309,9 @@ grad_factor = 1.0
 @layers = "reduce_mean.v1"
 
 [components.textcat_multilabel.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
@@ -542,14 +544,15 @@ nO = null
 width = ${components.tok2vec.model.encode.width}
 
 [components.textcat.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
 {% else -%}
 [components.textcat.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
 ngram_size = 1
 no_output_layer = false
@@ -570,15 +573,17 @@ nO = null
 width = ${components.tok2vec.model.encode.width}
 
 [components.textcat_multilabel.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 
 {% else -%}
 [components.textcat_multilabel.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 {%- endif %}

spacy/displacy/render.py

@@ -142,7 +142,25 @@ class SpanRenderer:
         spans (list): Individual entity spans and their start, end, label, kb_id and kb_url.
         title (str / None): Document title set in Doc.user_data['title'].
         """
-        per_token_info = []
+        per_token_info = self._assemble_per_token_info(tokens, spans)
+        markup = self._render_markup(per_token_info)
+        markup = TPL_SPANS.format(content=markup, dir=self.direction)
+        if title:
+            markup = TPL_TITLE.format(title=title) + markup
+        return markup
+
+    @staticmethod
+    def _assemble_per_token_info(
+        tokens: List[str], spans: List[Dict[str, Any]]
+    ) -> List[Dict[str, List[Dict[str, Any]]]]:
+        """Assembles token info used to generate markup in render_spans().
+        tokens (List[str]): Tokens in text.
+        spans (List[Dict[str, Any]]): Spans in text.
+        RETURNS (List[Dict[str, List[Dict, str, Any]]]): Per token info needed to render HTML markup for given tokens
+            and spans.
+        """
+        per_token_info: List[Dict[str, List[Dict[str, Any]]]] = []
+
         # we must sort so that we can correctly describe when spans need to "stack"
         # which is determined by their start token, then span length (longer spans on top),
         # then break any remaining ties with the span label
@@ -154,21 +172,22 @@ class SpanRenderer:
                 s["label"],
             ),
         )
+
         for s in spans:
             # this is the vertical 'slot' that the span will be rendered in
             # vertical_position = span_label_offset + (offset_step * (slot - 1))
             s["render_slot"] = 0
+
         for idx, token in enumerate(tokens):
             # Identify if a token belongs to a Span (and which) and if it's a
             # start token of said Span. We'll use this for the final HTML render
             token_markup: Dict[str, Any] = {}
             token_markup["text"] = token
-            concurrent_spans = 0
+            intersecting_spans: List[Dict[str, Any]] = []
             entities = []
             for span in spans:
                 ent = {}
                 if span["start_token"] <= idx < span["end_token"]:
-                    concurrent_spans += 1
                     span_start = idx == span["start_token"]
                     ent["label"] = span["label"]
                     ent["is_start"] = span_start
@@ -176,7 +195,12 @@ class SpanRenderer:
                         # When the span starts, we need to know how many other
                         # spans are on the 'span stack' and will be rendered.
                         # This value becomes the vertical render slot for this entire span
-                        span["render_slot"] = concurrent_spans
+                        span["render_slot"] = (
+                            intersecting_spans[-1]["render_slot"]
+                            if len(intersecting_spans)
+                            else 0
+                        ) + 1
+                    intersecting_spans.append(span)
                     ent["render_slot"] = span["render_slot"]
                     kb_id = span.get("kb_id", "")
                     kb_url = span.get("kb_url", "#")
@@ -193,11 +217,8 @@ class SpanRenderer:
                     span["render_slot"] = 0
             token_markup["entities"] = entities
             per_token_info.append(token_markup)
-        markup = self._render_markup(per_token_info)
+
-        markup = TPL_SPANS.format(content=markup, dir=self.direction)
+        return per_token_info
-        if title:
-            markup = TPL_TITLE.format(title=title) + markup
-        return markup
 
     def _render_markup(self, per_token_info: List[Dict[str, Any]]) -> str:
         """Render the markup from per-token information"""

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):
@@ -227,7 +228,6 @@ class Errors(metaclass=ErrorsWithCodes):
     E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). "
             "This usually happens when spaCy calls `nlp.{method}` with a custom "
             "component name that's not registered on the current language class. "
-            "If you're using a Transformer, make sure to install 'spacy-transformers'. "
             "If you're using a custom component, make sure you've added the "
             "decorator `@Language.component` (for function components) or "
             "`@Language.factory` (for class components).\n\nAvailable "
@@ -984,6 +984,10 @@ class Errors(metaclass=ErrorsWithCodes):
              "predicted docs when training {component}.")
     E1055 = ("The 'replace_listener' callback expects {num_params} parameters, "
              "but only callbacks with one or three parameters are supported")
+    E1056 = ("The `TextCatBOW` architecture expects a length of at least 1, was {length}.")
+    E1057 = ("The `TextCatReduce` architecture must be used with at least one "
+             "reduction. Please enable one of `use_reduce_first`, "
+             "`use_reduce_last`, `use_reduce_max` or `use_reduce_mean`.")
 
 
 # Deprecated model shortcuts, only used in errors and warnings

spacy/lang/bo/__init__.py

@@ -0,0 +1,16 @@
+from ...language import BaseDefaults, Language
+from .lex_attrs import LEX_ATTRS
+from .stop_words import STOP_WORDS
+
+
+class TibetanDefaults(BaseDefaults):
+    lex_attr_getters = LEX_ATTRS
+    stop_words = STOP_WORDS
+
+
+class Tibetan(Language):
+    lang = "bo"
+    Defaults = TibetanDefaults
+
+
+__all__ = ["Tibetan"]

spacy/lang/bo/examples.py

@@ -0,0 +1,16 @@
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.bo.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+
+sentences = [
+    "དོན་དུ་རྒྱ་མཚོ་བླ་མ་ཞེས་བྱ་ཞིང༌།",
+    "ཏཱ་ལའི་ཞེས་པ་ནི་སོག་སྐད་ཡིན་པ་དེ་བོད་སྐད་དུ་རྒྱ་མཚོའི་དོན་དུ་འཇུག",
+    "སོག་པོ་ཨལ་ཐན་རྒྱལ་པོས་རྒྱལ་དབང་བསོད་ནམས་རྒྱ་མཚོར་ཆེ་བསྟོད་ཀྱི་མཚན་གསོལ་བ་ཞིག་ཡིན་ཞིང༌།",
+    "རྗེས་སུ་རྒྱལ་བ་དགེ་འདུན་གྲུབ་དང༌། དགེ་འདུན་རྒྱ་མཚོ་སོ་སོར་ཡང་ཏཱ་ལའི་བླ་མའི་སྐུ་ཕྲེང་དང་པོ་དང༌།",
+    "གཉིས་པའི་མཚན་དེ་གསོལ་ཞིང༌།༸རྒྱལ་དབང་སྐུ་ཕྲེང་ལྔ་པས་དགའ་ལྡན་ཕོ་བྲང་གི་སྲིད་དབང་བཙུགས་པ་ནས་ཏཱ་ལའི་བླ་མ་ནི་བོད་ཀྱི་ཆོས་སྲིད་གཉིས་ཀྱི་དབུ་ཁྲིད་དུ་གྱུར་ཞིང་།",
+    "ད་ལྟའི་བར་ཏཱ་ལའི་བླ་མ་སྐུ་ཕྲེང་བཅུ་བཞི་བྱོན་ཡོད།",
+]

spacy/lang/bo/lex_attrs.py

@@ -0,0 +1,65 @@
+from ...attrs import LIKE_NUM
+
+# reference 1: https://en.wikipedia.org/wiki/Tibetan_numerals
+
+_num_words = [
+    "ཀླད་ཀོར་",
+    "གཅིག་",
+    "གཉིས་",
+    "གསུམ་",
+    "བཞི་",
+    "ལྔ་",
+    "དྲུག་",
+    "བདུན་",
+    "བརྒྱད་",
+    "དགུ་",
+    "བཅུ་",
+    "བཅུ་གཅིག་",
+    "བཅུ་གཉིས་",
+    "བཅུ་གསུམ་",
+    "བཅུ་བཞི་",
+    "བཅུ་ལྔ་",
+    "བཅུ་དྲུག་",
+    "བཅུ་བདུན་",
+    "བཅུ་པརྒྱད",
+    "བཅུ་དགུ་",
+    "ཉི་ཤུ་",
+    "སུམ་ཅུ",
+    "བཞི་བཅུ",
+    "ལྔ་བཅུ",
+    "དྲུག་ཅུ",
+    "བདུན་ཅུ",
+    "བརྒྱད་ཅུ",
+    "དགུ་བཅུ",
+    "བརྒྱ་",
+    "སྟོང་",
+    "ཁྲི་",
+    "ས་ཡ་",
+    "	བྱེ་བ་",
+    "དུང་ཕྱུར་",
+    "ཐེར་འབུམ་",
+    "ཐེར་འབུམ་ཆེན་པོ་",
+    "ཁྲག་ཁྲིག་",
+    "ཁྲག་ཁྲིག་ཆེན་པོ་",
+]
+
+
+def like_num(text):
+    """
+    Check if text resembles a number
+    """
+    if text.startswith(("+", "-", "±", "~")):
+        text = text[1:]
+    text = text.replace(",", "").replace(".", "")
+    if text.isdigit():
+        return True
+    if text.count("/") == 1:
+        num, denom = text.split("/")
+        if num.isdigit() and denom.isdigit():
+            return True
+    if text in _num_words:
+        return True
+    return False
+
+
+LEX_ATTRS = {LIKE_NUM: like_num}

spacy/lang/bo/stop_words.py

@@ -0,0 +1,198 @@
+# Source: https://zenodo.org/records/10148636
+
+STOP_WORDS = set(
+    """
+འི་
+།
+དུ་
+གིས་
+སོགས་
+ཏེ
+གི་
+རྣམས་
+ནི
+ཀུན་
+ཡི་
+འདི
+ཀྱི་
+སྙེད་
+པས་
+གཞན་
+ཀྱིས་
+ཡི
+ལ
+ནི་
+དང་
+སོགས
+ཅིང་
+ར
+དུ
+མི་
+སུ་
+བཅས་
+ཡོངས་
+ལས
+ཙམ་
+གྱིས་
+དེ་
+ཡང་
+མཐའ་དག་
+ཏུ་
+ཉིད་
+ས
+ཏེ་
+གྱི་
+སྤྱི
+དེ
+ཀ་
+ཡིན་
+ཞིང་
+འདི་
+རུང་
+རང་
+ཞིག་
+སྟེ
+སྟེ་
+ན་རེ
+ངམ
+ཤིང་
+དག་
+ཏོ
+རེ་
+འང་
+ཀྱང་
+ལགས་པ
+ཚུ
+དོ
+ཡིན་པ
+རེ
+ན་རེ་
+ཨེ་
+ཚང་མ
+ཐམས་ཅད་
+དམ་
+འོ་
+ཅིག་
+གྱིན་
+ཡིན
+ན
+ཁོ་ན་
+འམ་
+ཀྱིན་
+ལོ
+ཀྱིས
+བས་
+ལགས་
+ཤིག
+གིས
+ཀི་
+སྣ་ཚོགས་
+རྣམས
+སྙེད་པ
+ཡིས་
+གྱི
+གི
+བམ་
+ཤིག་
+རེ་རེ་
+ནམ
+མིན་
+ནམ་
+ངམ་
+རུ་
+འགའ་
+ཀུན
+ཤས་
+ཏུ
+ཡིས
+གིན་
+གམ་
+འོ
+ཡིན་པ་
+མིན
+ལགས
+གྱིས
+ཅང་
+འགའ
+སམ་
+ཞིག
+འང
+ལས་ཆེ་
+འཕྲལ་
+བར་
+རུ
+དང
+ཡ
+འག
+སམ
+ཀ
+ཅུང་ཟད་
+ཅིག
+ཉིད
+དུ་མ
+མ
+ཡིན་བ
+འམ
+མམ
+དམ
+དག
+ཁོ་ན
+ཀྱི
+ལམ
+ཕྱི་
+ནང་
+ཙམ
+ནོ་
+སོ་
+རམ་
+བོ་
+ཨང་
+ཕྱི
+ཏོ་
+ཚོ
+ལ་ལ་
+ཚོ་
+ཅིང
+མ་གི་
+གེ
+གོ
+ཡིན་ལུགས་
+རོ་
+བོ
+ལགས་པ་
+པས
+རབ་
+འི
+རམ
+བས
+གཞན
+སྙེད་པ་
+འབའ་
+མཾ་
+པོ
+ག་
+ག
+གམ
+སྤྱི་
+བམ
+མོ་
+ཙམ་པ་
+ཤ་སྟག་
+མམ་
+རེ་རེ
+སྙེད
+ཏམ་
+ངོ
+གྲང་
+ཏ་རེ
+ཏམ
+ཁ་
+ངེ་
+ཅོག་
+རིལ་
+ཉུང་ཤས་
+གིང་
+ཚ་
+ཀྱང
+""".split()
+)

spacy/lang/en/lex_attrs.py

@@ -6,7 +6,8 @@ _num_words = [
     "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen",
     "sixteen", "seventeen", "eighteen", "nineteen", "twenty", "thirty", "forty",
     "fifty", "sixty", "seventy", "eighty", "ninety", "hundred", "thousand",
-    "million", "billion", "trillion", "quadrillion", "gajillion", "bazillion"
+    "million", "billion", "trillion", "quadrillion", "quintillion", "sextillion",
+    "septillion", "octillion", "nonillion", "decillion", "gajillion", "bazillion"
 ]
 _ordinal_words = [
     "first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth",
@@ -14,7 +15,8 @@ _ordinal_words = [
     "fifteenth", "sixteenth", "seventeenth", "eighteenth", "nineteenth",
     "twentieth", "thirtieth", "fortieth", "fiftieth", "sixtieth", "seventieth",
     "eightieth", "ninetieth", "hundredth", "thousandth", "millionth", "billionth",
-    "trillionth", "quadrillionth", "gajillionth", "bazillionth"
+    "trillionth", "quadrillionth", "quintillionth", "sextillionth", "septillionth",
+    "octillionth", "nonillionth", "decillionth", "gajillionth", "bazillionth"
 ]
 # fmt: on
 

spacy/lang/fo/__init__.py

@@ -0,0 +1,18 @@
+from ...language import BaseDefaults, Language
+from ..punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+
+
+class FaroeseDefaults(BaseDefaults):
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    infixes = TOKENIZER_INFIXES
+    suffixes = TOKENIZER_SUFFIXES
+    prefixes = TOKENIZER_PREFIXES
+
+
+class Faroese(Language):
+    lang = "fo"
+    Defaults = FaroeseDefaults
+
+
+__all__ = ["Faroese"]

spacy/lang/fo/tokenizer_exceptions.py

@@ -0,0 +1,90 @@
+from ...symbols import ORTH
+from ...util import update_exc
+from ..tokenizer_exceptions import BASE_EXCEPTIONS
+
+_exc = {}
+
+for orth in [
+    "apr.",
+    "aug.",
+    "avgr.",
+    "árg.",
+    "ávís.",
+    "beinl.",
+    "blkv.",
+    "blaðkv.",
+    "blm.",
+    "blaðm.",
+    "bls.",
+    "blstj.",
+    "blaðstj.",
+    "des.",
+    "eint.",
+    "febr.",
+    "fyrrv.",
+    "góðk.",
+    "h.m.",
+    "innt.",
+    "jan.",
+    "kl.",
+    "m.a.",
+    "mðr.",
+    "mió.",
+    "nr.",
+    "nto.",
+    "nov.",
+    "nút.",
+    "o.a.",
+    "o.a.m.",
+    "o.a.tíl.",
+    "o.fl.",
+    "ff.",
+    "o.m.a.",
+    "o.o.",
+    "o.s.fr.",
+    "o.tíl.",
+    "o.ø.",
+    "okt.",
+    "omf.",
+    "pst.",
+    "ritstj.",
+    "sbr.",
+    "sms.",
+    "smst.",
+    "smb.",
+    "sb.",
+    "sbrt.",
+    "sp.",
+    "sept.",
+    "spf.",
+    "spsk.",
+    "t.e.",
+    "t.s.",
+    "t.s.s.",
+    "tlf.",
+    "tel.",
+    "tsk.",
+    "t.o.v.",
+    "t.d.",
+    "uml.",
+    "ums.",
+    "uppl.",
+    "upprfr.",
+    "uppr.",
+    "útg.",
+    "útl.",
+    "útr.",
+    "vanl.",
+    "v.",
+    "v.h.",
+    "v.ø.o.",
+    "viðm.",
+    "viðv.",
+    "vm.",
+    "v.m.",
+]:
+    _exc[orth] = [{ORTH: orth}]
+    capitalized = orth.capitalize()
+    _exc[capitalized] = [{ORTH: capitalized}]
+
+TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)

spacy/lang/gd/__init__.py

@@ -0,0 +1,18 @@
+from typing import Optional
+
+from ...language import BaseDefaults, Language
+from .stop_words import STOP_WORDS
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+
+
+class ScottishDefaults(BaseDefaults):
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    stop_words = STOP_WORDS
+
+
+class Scottish(Language):
+    lang = "gd"
+    Defaults = ScottishDefaults
+
+
+__all__ = ["Scottish"]

spacy/lang/gd/stop_words.py

@@ -0,0 +1,388 @@
+STOP_WORDS = set(
+    """
+'ad
+'ar
+'d # iad
+'g # ag
+'ga
+'gam
+'gan
+'gar
+'gur
+'m # am
+'n # an
+'n seo
+'na
+'nad
+'nam
+'nan
+'nar
+'nuair
+'nur
+'s
+'sa
+'san
+'sann
+'se
+'sna
+a
+a'
+a'd # agad
+a'm # agam
+a-chèile
+a-seo
+a-sin
+a-siud
+a chionn
+a chionn 's
+a chèile
+a chéile
+a dh'
+a h-uile
+a seo
+ac' # aca
+aca
+aca-san
+acasan
+ach
+ag
+agad
+agad-sa
+agads'
+agadsa
+agaibh
+agaibhse
+againn
+againne
+agam
+agam-sa
+agams'
+agamsa
+agus
+aice
+aice-se
+aicese
+aig
+aig' # aige
+aige
+aige-san
+aigesan
+air
+air-san
+air neo
+airsan
+am
+an
+an seo
+an sin
+an siud
+an uair
+ann
+ann a
+ann a'
+ann a shin
+ann am
+ann an
+annad
+annam
+annam-s'
+annamsa
+anns
+anns an
+annta
+aon
+ar
+as
+asad
+asda
+asta
+b'
+bho
+bhon
+bhuaidhe # bhuaithe
+bhuainn
+bhuaipe
+bhuaithe
+bhuapa
+bhur
+brì
+bu
+c'à
+car son
+carson
+cha
+chan
+chionn
+choir
+chon
+chun
+chèile
+chéile
+chòir
+cia mheud
+ciamar
+co-dhiubh
+cuide
+cuin
+cuin'
+cuine
+cà
+cà'
+càil
+càit
+càit'
+càite
+cò
+cò mheud
+có
+d'
+da
+de
+dh'
+dha
+dhaibh
+dhaibh-san
+dhaibhsan
+dhan
+dhasan
+dhe
+dhen
+dheth
+dhi
+dhiom
+dhiot
+dhith
+dhiubh
+dhomh
+dhomh-s'
+dhomhsa
+dhu'sa # dhut-sa
+dhuibh
+dhuibhse
+dhuinn
+dhuinne
+dhuit
+dhut
+dhutsa
+dhut-sa
+dhà
+dhà-san
+dhàsan
+dhòmhsa
+diubh
+do
+docha
+don
+dà
+dè
+dè mar
+dé
+dé mar
+dòch'
+dòcha
+e
+eadar
+eatarra
+eatorra
+eile
+esan
+fa
+far
+feud
+fhad
+fheudar
+fhearr
+fhein
+fheudar
+fheàrr
+fhèin
+fhéin
+fhìn
+fo
+fodha
+fodhainn
+foipe
+fon
+fèin
+ga
+gach
+gam
+gan
+ge brith
+ged
+gu
+gu dè
+gu ruige
+gun
+gur
+gus
+i
+iad
+iadsan
+innte
+is
+ise
+le
+leam
+leam-sa
+leamsa
+leat
+leat-sa
+leatha
+leatsa
+leibh
+leis
+leis-san
+leoth'
+leotha
+leotha-san
+linn
+m'
+m'a
+ma
+mac
+man
+mar
+mas
+mathaid
+mi
+mis'
+mise
+mo
+mu
+mu 'n
+mun
+mur
+mura
+mus
+na
+na b'
+na bu
+na iad
+nach
+nad
+nam
+nan
+nar
+nas
+neo
+no
+nuair
+o
+o'n
+oir
+oirbh
+oirbh-se
+oirnn
+oirnne
+oirre
+on
+orm
+orm-sa
+ormsa
+orra
+orra-san
+orrasan
+ort
+os
+r'
+ri
+ribh
+rinn
+ris
+rithe
+rithe-se
+rium
+rium-sa
+riums'
+riumsa
+riut
+riuth'
+riutha
+riuthasan
+ro
+ro'n
+roimh
+roimhe
+romhainn
+romham
+romhpa
+ron
+ruibh
+ruinn
+ruinne
+sa
+san
+sann
+se
+seach
+seo
+seothach
+shin
+sibh
+sibh-se
+sibhse
+sin
+sineach
+sinn
+sinne
+siod
+siodach
+siud
+siudach
+sna # ann an
+sè
+t'
+tarsaing
+tarsainn
+tarsuinn
+thar
+thoigh
+thro
+thu
+thuc'
+thuca
+thugad
+thugaibh
+thugainn
+thugam
+thugamsa
+thuice
+thuige
+thus'
+thusa
+timcheall
+toigh
+toil
+tro
+tro' # troimh
+troimh
+troimhe
+tron
+tu
+tusa
+uair
+ud
+ugaibh
+ugam-s'
+ugam-sa
+uice
+uige
+uige-san
+umad
+unnta # ann an
+ur
+urrainn
+à
+às
+àsan
+á
+ás
+è
+ì
+ò
+ó
+""".split(
+        "\n"
+    )
+)

spacy/lang/hr/lemma_lookup_license.txt

@@ -1,5 +1,5 @@
 The list of Croatian lemmas was extracted from the reldi-tagger repository (https://github.com/clarinsi/reldi-tagger).
-Reldi-tagger is licesned under the Apache 2.0 licence.
+Reldi-tagger is licensed under the Apache 2.0 licence.
 
 @InProceedings{ljubesic16-new,
   author = {Nikola Ljubešić and Filip Klubička and Željko Agić and Ivo-Pavao Jazbec},

spacy/lang/ht/__init__.py

@@ -0,0 +1,52 @@
+from typing import Callable, Optional
+
+from thinc.api import Model
+
+from ...language import BaseDefaults, Language
+from .lemmatizer import HaitianCreoleLemmatizer
+from .lex_attrs import LEX_ATTRS
+from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
+from .stop_words import STOP_WORDS
+from .syntax_iterators import SYNTAX_ITERATORS
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+from .tag_map import TAG_MAP
+
+
+class HaitianCreoleDefaults(BaseDefaults):
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    prefixes = TOKENIZER_PREFIXES
+    infixes = TOKENIZER_INFIXES
+    suffixes = TOKENIZER_SUFFIXES
+    lex_attr_getters = LEX_ATTRS
+    syntax_iterators = SYNTAX_ITERATORS
+    stop_words = STOP_WORDS
+    tag_map = TAG_MAP
+
+class HaitianCreole(Language):
+    lang = "ht"
+    Defaults = HaitianCreoleDefaults
+
+@HaitianCreole.factory(
+    "lemmatizer",
+    assigns=["token.lemma"],
+    default_config={
+        "model": None,
+        "mode": "rule",
+        "overwrite": False,
+        "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
+    },
+    default_score_weights={"lemma_acc": 1.0},
+)
+def make_lemmatizer(
+    nlp: Language,
+    model: Optional[Model],
+    name: str,
+    mode: str,
+    overwrite: bool,
+    scorer: Optional[Callable],
+):
+    return HaitianCreoleLemmatizer(
+        nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
+    )
+
+__all__ = ["HaitianCreole"]

spacy/lang/ht/examples.py

@@ -0,0 +1,18 @@
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.ht.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+
+sentences = [
+    "Apple ap panse achte yon demaraj nan Wayòm Ini pou $1 milya dola",
+    "Machin otonòm fè responsablite asirans lan ale sou men fabrikan yo",
+    "San Francisco ap konsidere entèdi robo ki livre sou twotwa yo",
+    "Lond se yon gwo vil nan Wayòm Ini",
+    "Kote ou ye?",
+    "Kilès ki prezidan Lafrans?",
+    "Ki kapital Etazini?",
+    "Kile Barack Obama te fèt?",
+]

spacy/lang/ht/lemmatizer.py

@@ -0,0 +1,51 @@
+from typing import List, Tuple
+
+from ...pipeline import Lemmatizer
+from ...tokens import Token
+from ...lookups import Lookups
+
+
+class HaitianCreoleLemmatizer(Lemmatizer):
+    """
+    Minimal Haitian Creole lemmatizer.
+    Returns a word's base form based on rules and lookup,
+    or defaults to the original form.
+    """
+
+    def is_base_form(self, token: Token) -> bool:
+        morph = token.morph.to_dict()
+        upos = token.pos_.lower()
+
+        # Consider unmarked forms to be base
+        if upos in {"noun", "verb", "adj", "adv"}:
+            if not morph:
+                return True
+            if upos == "noun" and morph.get("Number") == "Sing":
+                return True
+            if upos == "verb" and morph.get("VerbForm") == "Inf":
+                return True
+            if upos == "adj" and morph.get("Degree") == "Pos":
+                return True
+        return False
+
+    def rule_lemmatize(self, token: Token) -> List[str]:
+        string = token.text.lower()
+        pos = token.pos_.lower()
+        cache_key = (token.orth, token.pos)
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+
+        forms = []
+
+        # fallback rule: just return lowercased form
+        forms.append(string)
+
+        self.cache[cache_key] = forms
+        return forms
+
+    @classmethod
+    def get_lookups_config(cls, mode: str) -> Tuple[List[str], List[str]]:
+        if mode == "rule":
+            required = ["lemma_lookup", "lemma_rules", "lemma_exc", "lemma_index"]
+            return (required, [])
+        return super().get_lookups_config(mode)

spacy/lang/ht/lex_attrs.py

@@ -0,0 +1,78 @@
+from ...attrs import LIKE_NUM, NORM
+
+# Cardinal numbers in Creole
+_num_words = set(
+    """
+zewo youn en de twa kat senk sis sèt uit nèf dis
+onz douz trèz katoz kenz sèz disèt dizwit diznèf
+vent trant karant sinkant swasant swasann-dis
+san mil milyon milya
+""".split()
+)
+
+# Ordinal numbers in Creole (some are French-influenced, some simplified)
+_ordinal_words = set(
+    """
+premye dezyèm twazyèm katryèm senkyèm sizyèm sètvyèm uitvyèm nèvyèm dizyèm
+onzèm douzyèm trèzyèm katozyèm kenzèm sèzyèm disetyèm dizwityèm diznèvyèm
+ventyèm trantyèm karantyèm sinkantyèm swasantyèm
+swasann-disyèm santyèm milyèm milyonnyèm milyadyèm
+""".split()
+)
+
+NORM_MAP = {
+    "'m": "mwen",
+    "'w": "ou",
+    "'l": "li",
+    "'n": "nou",
+    "'y": "yo",
+    "’m": "mwen",
+    "’w": "ou",
+    "’l": "li",
+    "’n": "nou",
+    "’y": "yo",
+    "m": "mwen",
+    "n": "nou",
+    "l": "li",
+    "y": "yo",
+    "w": "ou",
+    "t": "te",
+    "k": "ki",
+    "p": "pa",
+    "M": "Mwen",
+    "N": "Nou",
+    "L": "Li",
+    "Y": "Yo",
+    "W": "Ou",
+    "T": "Te",
+    "K": "Ki",
+    "P": "Pa",
+}
+
+def like_num(text):
+    text = text.strip().lower()
+    if text.startswith(("+", "-", "±", "~")):
+        text = text[1:]
+    text = text.replace(",", "").replace(".", "")
+    if text.isdigit():
+        return True
+    if text.count("/") == 1:
+        num, denom = text.split("/")
+        if num.isdigit() and denom.isdigit():
+            return True
+    if text in _num_words:
+        return True
+    if text in _ordinal_words:
+        return True
+    # Handle things like "3yèm", "10yèm", "25yèm", etc.
+    if text.endswith("yèm") and text[:-3].isdigit():
+        return True
+    return False
+
+def norm_custom(text):
+    return NORM_MAP.get(text, text.lower())
+
+LEX_ATTRS = {
+    LIKE_NUM: like_num,
+    NORM: norm_custom,
+}

spacy/lang/ht/punctuation.py

@@ -0,0 +1,43 @@
+from ..char_classes import (
+    ALPHA,
+    ALPHA_LOWER,
+    ALPHA_UPPER,
+    CONCAT_QUOTES,
+    HYPHENS,
+    LIST_PUNCT,
+    LIST_QUOTES,
+    LIST_ELLIPSES,
+    LIST_ICONS,
+    merge_chars,
+)
+
+ELISION = "'’".replace(" ", "")
+
+_prefixes_elision = "m n l y t k w"
+_prefixes_elision += " " + _prefixes_elision.upper()
+
+TOKENIZER_PREFIXES = LIST_PUNCT + LIST_QUOTES + [
+    r"(?:({pe})[{el}])(?=[{a}])".format(
+        a=ALPHA, el=ELISION, pe=merge_chars(_prefixes_elision)
+    )
+]
+
+TOKENIZER_SUFFIXES = LIST_PUNCT + LIST_QUOTES + LIST_ELLIPSES + [
+    r"(?<=[0-9])%",  # numbers like 10%
+    r"(?<=[0-9])(?:{h})".format(h=HYPHENS),  # hyphens after numbers
+    r"(?<=[{a}])['’]".format(a=ALPHA),  # apostrophes after letters
+    r"(?<=[{a}])['’][mwlnytk](?=\s|$)".format(a=ALPHA),  # contractions
+    r"(?<=[{a}0-9])\)",  # right parenthesis after letter/number
+    r"(?<=[{a}])\.(?=\s|$)".format(a=ALPHA),  # period after letter if space or end of string
+    r"(?<=\))[\.\?!]",  # punctuation immediately after right parenthesis
+]
+
+TOKENIZER_INFIXES = LIST_ELLIPSES + LIST_ICONS + [
+    r"(?<=[0-9])[+\-\*^](?=[0-9-])",
+    r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
+        al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
+    ),
+    r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+    r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
+    r"(?<=[{a}][{el}])(?=[{a}])".format(a=ALPHA, el=ELISION),
+]

spacy/lang/ht/stop_words.py

@@ -0,0 +1,50 @@
+STOP_WORDS = set(
+    """
+a ak an ankò ant apre ap atò avan avanlè
+byen bò byenke
+
+chak
+
+de depi deja deja
+
+e en epi èske
+
+fò fòk
+
+gen genyen
+
+ki kisa kilès kote koukou konsa konbyen konn konnen kounye kouman
+
+la l laa le lè li lye lò
+
+m m' mwen
+
+nan nap nou n'
+
+ou oumenm
+
+pa paske pami pandan pito pou pral preske pwiske
+
+se selman si sou sòt
+
+ta tap tankou te toujou tou tan tout toutotan twòp tèl
+
+w w' wi wè
+
+y y' yo yon yonn
+
+non o oh eh
+
+sa san si swa si
+
+men mèsi oswa osinon
+
+"""
+.split()
+)
+
+# Add common contractions, with and without apostrophe variants
+contractions = ["m'", "n'", "w'", "y'", "l'", "t'", "k'"]
+for apostrophe in ["'", "’", "‘"]:
+    for word in contractions:
+        STOP_WORDS.add(word.replace("'", apostrophe))

spacy/lang/ht/syntax_iterators.py

@@ -0,0 +1,74 @@
+from typing import Iterator, Tuple, Union
+
+from ...errors import Errors
+from ...symbols import NOUN, PRON, PROPN
+from ...tokens import Doc, Span
+
+
+def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]:
+    """
+    Detect base noun phrases from a dependency parse for Haitian Creole.
+    Works on both Doc and Span objects.
+    """
+
+    # Core nominal dependencies common in Haitian Creole
+    labels = [
+        "nsubj",
+        "obj",
+        "obl",
+        "nmod",
+        "appos",
+        "ROOT",
+    ]
+
+    # Modifiers to optionally include in chunk (to the right)
+    post_modifiers = ["compound", "flat", "flat:name", "fixed"]
+
+    doc = doclike.doc
+    if not doc.has_annotation("DEP"):
+        raise ValueError(Errors.E029)
+
+    np_deps = {doc.vocab.strings.add(label) for label in labels}
+    np_mods = {doc.vocab.strings.add(mod) for mod in post_modifiers}
+    conj_label = doc.vocab.strings.add("conj")
+    np_label = doc.vocab.strings.add("NP")
+    adp_pos = doc.vocab.strings.add("ADP")
+    cc_pos = doc.vocab.strings.add("CCONJ")
+
+    prev_end = -1
+    for i, word in enumerate(doclike):
+        if word.pos not in (NOUN, PROPN, PRON):
+            continue
+        if word.left_edge.i <= prev_end:
+            continue
+
+        if word.dep in np_deps:
+            right_end = word
+            # expand to include known modifiers to the right
+            for child in word.rights:
+                if child.dep in np_mods:
+                    right_end = child.right_edge
+                elif child.pos == NOUN:
+                    right_end = child.right_edge
+
+            left_index = word.left_edge.i
+            # Skip prepositions at the start
+            if word.left_edge.pos == adp_pos:
+                left_index += 1
+
+            prev_end = right_end.i
+            yield left_index, right_end.i + 1, np_label
+
+        elif word.dep == conj_label:
+            head = word.head
+            while head.dep == conj_label and head.head.i < head.i:
+                head = head.head
+            if head.dep in np_deps:
+                left_index = word.left_edge.i
+                if word.left_edge.pos == cc_pos:
+                    left_index += 1
+                prev_end = word.i
+                yield left_index, word.i + 1, np_label
+
+
+SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

spacy/lang/ht/tag_map.py

@@ -0,0 +1,21 @@
+from spacy.symbols import NOUN, VERB, AUX, ADJ, ADV, PRON, DET, ADP, SCONJ, CCONJ, PART, INTJ, NUM, PROPN, PUNCT, SYM, X
+
+TAG_MAP = {
+    "NOUN": {"pos": NOUN},
+    "VERB": {"pos": VERB},
+    "AUX": {"pos": AUX},
+    "ADJ": {"pos": ADJ},
+    "ADV": {"pos": ADV},
+    "PRON": {"pos": PRON},
+    "DET": {"pos": DET},
+    "ADP": {"pos": ADP},
+    "SCONJ": {"pos": SCONJ},
+    "CCONJ": {"pos": CCONJ},
+    "PART": {"pos": PART},
+    "INTJ": {"pos": INTJ},
+    "NUM": {"pos": NUM},
+    "PROPN": {"pos": PROPN},
+    "PUNCT": {"pos": PUNCT},
+    "SYM": {"pos": SYM},
+    "X": {"pos": X},
+}

spacy/lang/ht/tokenizer_exceptions.py

@@ -0,0 +1,121 @@
+from spacy.symbols import ORTH, NORM
+
+def make_variants(base, first_norm, second_orth, second_norm):
+    return {
+        base: [
+            {ORTH: base.split("'")[0] + "'", NORM: first_norm},
+            {ORTH: second_orth, NORM: second_norm},
+        ],
+        base.capitalize(): [
+            {ORTH: base.split("'")[0].capitalize() + "'", NORM: first_norm.capitalize()},
+            {ORTH: second_orth, NORM: second_norm},
+        ]
+    }
+
+TOKENIZER_EXCEPTIONS = {
+    "Dr.": [{ORTH: "Dr."}]
+}
+
+# Apostrophe forms
+TOKENIZER_EXCEPTIONS.update(make_variants("m'ap", "mwen", "ap", "ap"))
+TOKENIZER_EXCEPTIONS.update(make_variants("n'ap", "nou", "ap", "ap"))
+TOKENIZER_EXCEPTIONS.update(make_variants("l'ap", "li", "ap", "ap"))
+TOKENIZER_EXCEPTIONS.update(make_variants("y'ap", "yo", "ap", "ap"))
+TOKENIZER_EXCEPTIONS.update(make_variants("m'te", "mwen", "te", "te"))
+TOKENIZER_EXCEPTIONS.update(make_variants("m'pral", "mwen", "pral", "pral"))
+TOKENIZER_EXCEPTIONS.update(make_variants("w'ap", "ou", "ap", "ap"))
+TOKENIZER_EXCEPTIONS.update(make_variants("k'ap", "ki", "ap", "ap"))
+TOKENIZER_EXCEPTIONS.update(make_variants("p'ap", "pa", "ap", "ap"))
+TOKENIZER_EXCEPTIONS.update(make_variants("t'ap", "te", "ap", "ap"))
+
+# Non-apostrophe contractions (with capitalized variants)
+TOKENIZER_EXCEPTIONS.update({
+    "map": [
+        {ORTH: "m", NORM: "mwen"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "Map": [
+        {ORTH: "M", NORM: "Mwen"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "lem": [
+        {ORTH: "le", NORM: "le"},
+        {ORTH: "m", NORM: "mwen"},
+    ],
+    "Lem": [
+        {ORTH: "Le", NORM: "Le"},
+        {ORTH: "m", NORM: "mwen"},
+    ],
+    "lew": [
+        {ORTH: "le", NORM: "le"},
+        {ORTH: "w", NORM: "ou"},
+    ],
+    "Lew": [
+        {ORTH: "Le", NORM: "Le"},
+        {ORTH: "w", NORM: "ou"},
+    ],
+    "nap": [
+        {ORTH: "n", NORM: "nou"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "Nap": [
+        {ORTH: "N", NORM: "Nou"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "lap": [
+        {ORTH: "l", NORM: "li"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "Lap": [
+        {ORTH: "L", NORM: "Li"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "yap": [
+        {ORTH: "y", NORM: "yo"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "Yap": [
+        {ORTH: "Y", NORM: "Yo"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "mte": [
+        {ORTH: "m", NORM: "mwen"},
+        {ORTH: "te", NORM: "te"},
+    ],
+    "Mte": [
+        {ORTH: "M", NORM: "Mwen"},
+        {ORTH: "te", NORM: "te"},
+    ],
+    "mpral": [
+        {ORTH: "m", NORM: "mwen"},
+        {ORTH: "pral", NORM: "pral"},
+    ],
+    "Mpral": [
+        {ORTH: "M", NORM: "Mwen"},
+        {ORTH: "pral", NORM: "pral"},
+    ],
+    "wap": [
+        {ORTH: "w", NORM: "ou"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "Wap": [
+        {ORTH: "W", NORM: "Ou"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "kap": [
+        {ORTH: "k", NORM: "ki"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "Kap": [
+        {ORTH: "K", NORM: "Ki"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "tap": [
+        {ORTH: "t", NORM: "te"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+    "Tap": [
+        {ORTH: "T", NORM: "Te"},
+        {ORTH: "ap", NORM: "ap"},
+    ],
+})

spacy/lang/ja/__init__.py

@@ -32,7 +32,6 @@ split_mode = null
 """
 
 
-@registry.tokenizers("spacy.ja.JapaneseTokenizer")
 def create_tokenizer(split_mode: Optional[str] = None):
     def japanese_tokenizer_factory(nlp):
         return JapaneseTokenizer(nlp.vocab, split_mode=split_mode)

spacy/lang/kmr/__init__.py

@@ -0,0 +1,16 @@
+from ...language import BaseDefaults, Language
+from .lex_attrs import LEX_ATTRS
+from .stop_words import STOP_WORDS
+
+
+class KurmanjiDefaults(BaseDefaults):
+    stop_words = STOP_WORDS
+    lex_attr_getters = LEX_ATTRS
+
+
+class Kurmanji(Language):
+    lang = "kmr"
+    Defaults = KurmanjiDefaults
+
+
+__all__ = ["Kurmanji"]

spacy/lang/kmr/examples.py

@@ -0,0 +1,17 @@
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.kmr.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+sentences = [
+    "Berê mirovan her tim li geşedana pêşerojê ye",  # People's gaze is always on the development of the future
+    "Kawa Nemir di 14 salan de Ulysses wergerand Kurmancî.",  # Kawa Nemir translated Ulysses into Kurmanji in 14 years.
+    "Mem Ararat hunermendekî Kurd yê bi nav û deng e.",  # Mem Ararat is a famous Kurdish artist
+    "Firat Cewerî 40 sal e pirtûkên Kurdî dinivîsîne.",  # Firat Ceweri has been writing Kurdish books for 40 years
+    "Rojnamegerê ciwan nûçeyeke balkêş li ser rewşa aborî nivîsand",  # The young journalist wrote an interesting news article about the economic situation
+    "Sektora çandiniyê beşeke giring a belavkirina gaza serayê li seranserê cîhanê pêk tîne",  # The agricultural sector constitutes an important part of greenhouse gas emissions worldwide
+    "Xwendekarên jêhatî di pêşbaziya matematîkê de serkeftî bûn",  # Talented students succeeded in the mathematics competition
+    "Ji ber ji tunebûnê bavê min xwişkeke min nedan xwendin ew ji min re bû derd û kulek.",  # Because of poverty, my father didn't send my sister to school, which became a pain and sorrow for me
+]

spacy/lang/kmr/lex_attrs.py

@@ -0,0 +1,138 @@
+from ...attrs import LIKE_NUM
+
+_num_words = [
+    "sifir",
+    "yek",
+    "du",
+    "sê",
+    "çar",
+    "pênc",
+    "şeş",
+    "heft",
+    "heşt",
+    "neh",
+    "deh",
+    "yazde",
+    "dazde",
+    "sêzde",
+    "çarde",
+    "pazde",
+    "şazde",
+    "hevde",
+    "hejde",
+    "nozde",
+    "bîst",
+    "sî",
+    "çil",
+    "pêncî",
+    "şêst",
+    "heftê",
+    "heştê",
+    "nod",
+    "sed",
+    "hezar",
+    "milyon",
+    "milyar",
+]
+
+_ordinal_words = [
+    "yekem",
+    "yekemîn",
+    "duyem",
+    "duyemîn",
+    "sêyem",
+    "sêyemîn",
+    "çarem",
+    "çaremîn",
+    "pêncem",
+    "pêncemîn",
+    "şeşem",
+    "şeşemîn",
+    "heftem",
+    "heftemîn",
+    "heştem",
+    "heştemîn",
+    "nehem",
+    "nehemîn",
+    "dehem",
+    "dehemîn",
+    "yazdehem",
+    "yazdehemîn",
+    "dazdehem",
+    "dazdehemîn",
+    "sêzdehem",
+    "sêzdehemîn",
+    "çardehem",
+    "çardehemîn",
+    "pazdehem",
+    "pazdehemîn",
+    "şanzdehem",
+    "şanzdehemîn",
+    "hevdehem",
+    "hevdehemîn",
+    "hejdehem",
+    "hejdehemîn",
+    "nozdehem",
+    "nozdehemîn",
+    "bîstem",
+    "bîstemîn",
+    "sîyem",
+    "sîyemîn",
+    "çilem",
+    "çilemîn",
+    "pêncîyem",
+    "pênciyemîn",
+    "şêstem",
+    "şêstemîn",
+    "heftêyem",
+    "heftêyemîn",
+    "heştêyem",
+    "heştêyemîn",
+    "notem",
+    "notemîn",
+    "sedem",
+    "sedemîn",
+    "hezarem",
+    "hezaremîn",
+    "milyonem",
+    "milyonemîn",
+    "milyarem",
+    "milyaremîn",
+]
+
+
+def like_num(text):
+    if text.startswith(("+", "-", "±", "~")):
+        text = text[1:]
+    text = text.replace(",", "").replace(".", "")
+    if text.isdigit():
+        return True
+    if text.count("/") == 1:
+        num, denom = text.split("/")
+        if num.isdigit() and denom.isdigit():
+            return True
+    text_lower = text.lower()
+    if text_lower in _num_words:
+        return True
+
+    # Check ordinal number
+    if text_lower in _ordinal_words:
+        return True
+
+    if is_digit(text_lower):
+        return True
+
+    return False
+
+
+def is_digit(text):
+    endings = ("em", "yem", "emîn", "yemîn")
+    for ending in endings:
+        to = len(ending)
+        if text.endswith(ending) and text[:-to].isdigit():
+            return True
+
+    return False
+
+
+LEX_ATTRS = {LIKE_NUM: like_num}

spacy/lang/kmr/stop_words.py

@@ -0,0 +1,44 @@
+STOP_WORDS = set(
+    """
+û
+li
+bi
+di
+da
+de
+ji
+ku
+ew
+ez
+tu
+em
+hûn
+ew
+ev
+min
+te
+wî
+wê
+me
+we
+wan
+vê
+vî
+va
+çi
+kî
+kê
+çawa
+çima
+kengî
+li ku
+çend
+çiqas
+her
+hin
+gelek
+hemû
+kes
+tişt
+""".split()
+)

spacy/lang/ko/__init__.py

@@ -20,7 +20,6 @@ DEFAULT_CONFIG = """
 """
 
 
-@registry.tokenizers("spacy.ko.KoreanTokenizer")
 def create_tokenizer():
     def korean_tokenizer_factory(nlp):
         return KoreanTokenizer(nlp.vocab)

spacy/lang/mk/__init__.py

@@ -24,12 +24,6 @@ class MacedonianDefaults(BaseDefaults):
     tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
     stop_words = STOP_WORDS
 
-    @classmethod
-    def create_lemmatizer(cls, nlp=None, lookups=None):
-        if lookups is None:
-            lookups = Lookups()
-        return MacedonianLemmatizer(lookups)
-
 
 class Macedonian(Language):
     lang = "mk"

spacy/lang/nn/__init__.py

@@ -0,0 +1,20 @@
+from ...language import BaseDefaults, Language
+from ..nb import SYNTAX_ITERATORS
+from .punctuation import TOKENIZER_INFIXES, TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+
+
+class NorwegianNynorskDefaults(BaseDefaults):
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    prefixes = TOKENIZER_PREFIXES
+    infixes = TOKENIZER_INFIXES
+    suffixes = TOKENIZER_SUFFIXES
+    syntax_iterators = SYNTAX_ITERATORS
+
+
+class NorwegianNynorsk(Language):
+    lang = "nn"
+    Defaults = NorwegianNynorskDefaults
+
+
+__all__ = ["NorwegianNynorsk"]

spacy/lang/nn/examples.py

@@ -0,0 +1,15 @@
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.nn.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+
+# sentences taken from Omsetjingsminne frå Nynorsk pressekontor 2022 (https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-80/)
+sentences = [
+    "Konseptet går ut på at alle tre omgangar tel, alle hopparar må stille i kvalifiseringa og poengsummen skal telje.",
+    "Det er ein meir enn i same periode i fjor.",
+    "Det har lava ned enorme snømengder i store delar av Europa den siste tida.",
+    "Akhtar Chaudhry er ikkje innstilt på Oslo-lista til SV, men utfordrar Heikki Holmås om førsteplassen.",
+]

spacy/lang/nn/punctuation.py

@@ -0,0 +1,74 @@
+from ..char_classes import (
+    ALPHA,
+    ALPHA_LOWER,
+    ALPHA_UPPER,
+    CONCAT_QUOTES,
+    CURRENCY,
+    LIST_CURRENCY,
+    LIST_ELLIPSES,
+    LIST_ICONS,
+    LIST_PUNCT,
+    LIST_QUOTES,
+    PUNCT,
+    UNITS,
+)
+from ..punctuation import TOKENIZER_SUFFIXES
+
+_quotes = CONCAT_QUOTES.replace("'", "")
+_list_punct = [x for x in LIST_PUNCT if x != "#"]
+_list_icons = [x for x in LIST_ICONS if x != "°"]
+_list_icons = [x.replace("\\u00B0", "") for x in _list_icons]
+_list_quotes = [x for x in LIST_QUOTES if x != "\\'"]
+
+
+_prefixes = (
+    ["§", "%", "=", "—", "–", r"\+(?![0-9])"]
+    + _list_punct
+    + LIST_ELLIPSES
+    + LIST_QUOTES
+    + LIST_CURRENCY
+    + LIST_ICONS
+)
+
+
+_infixes = (
+    LIST_ELLIPSES
+    + _list_icons
+    + [
+        r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
+        r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}])[:<>=/](?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}])([{q}\)\]\(\[])(?=[{a}])".format(a=ALPHA, q=_quotes),
+        r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
+    ]
+)
+
+_suffixes = (
+    LIST_PUNCT
+    + LIST_ELLIPSES
+    + _list_quotes
+    + _list_icons
+    + ["—", "–"]
+    + [
+        r"(?<=[0-9])\+",
+        r"(?<=°[FfCcKk])\.",
+        r"(?<=[0-9])(?:{c})".format(c=CURRENCY),
+        r"(?<=[0-9])(?:{u})".format(u=UNITS),
+        r"(?<=[{al}{e}{p}(?:{q})])\.".format(
+            al=ALPHA_LOWER, e=r"%²\-\+", q=_quotes, p=PUNCT
+        ),
+        r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
+    ]
+    + [r"(?<=[^sSxXzZ])'"]
+)
+_suffixes += [
+    suffix
+    for suffix in TOKENIZER_SUFFIXES
+    if suffix not in ["'s", "'S", "’s", "’S", r"\'"]
+]
+
+
+TOKENIZER_PREFIXES = _prefixes
+TOKENIZER_INFIXES = _infixes
+TOKENIZER_SUFFIXES = _suffixes

spacy/lang/nn/tokenizer_exceptions.py

@@ -0,0 +1,228 @@
+from ...symbols import NORM, ORTH
+from ...util import update_exc
+from ..tokenizer_exceptions import BASE_EXCEPTIONS
+
+_exc = {}
+
+
+for exc_data in [
+    {ORTH: "jan.", NORM: "januar"},
+    {ORTH: "feb.", NORM: "februar"},
+    {ORTH: "mar.", NORM: "mars"},
+    {ORTH: "apr.", NORM: "april"},
+    {ORTH: "jun.", NORM: "juni"},
+    # note: "jul." is in the simple list below without a NORM exception
+    {ORTH: "aug.", NORM: "august"},
+    {ORTH: "sep.", NORM: "september"},
+    {ORTH: "okt.", NORM: "oktober"},
+    {ORTH: "nov.", NORM: "november"},
+    {ORTH: "des.", NORM: "desember"},
+]:
+    _exc[exc_data[ORTH]] = [exc_data]
+
+
+for orth in [
+    "Ap.",
+    "Aq.",
+    "Ca.",
+    "Chr.",
+    "Co.",
+    "Dr.",
+    "F.eks.",
+    "Fr.p.",
+    "Frp.",
+    "Grl.",
+    "Kr.",
+    "Kr.F.",
+    "Kr.F.s",
+    "Mr.",
+    "Mrs.",
+    "Pb.",
+    "Pr.",
+    "Sp.",
+    "St.",
+    "a.m.",
+    "ad.",
+    "adm.dir.",
+    "adr.",
+    "b.c.",
+    "bl.a.",
+    "bla.",
+    "bm.",
+    "bnr.",
+    "bto.",
+    "c.c.",
+    "ca.",
+    "cand.mag.",
+    "co.",
+    "d.d.",
+    "d.m.",
+    "d.y.",
+    "dept.",
+    "dr.",
+    "dr.med.",
+    "dr.philos.",
+    "dr.psychol.",
+    "dss.",
+    "dvs.",
+    "e.Kr.",
+    "e.l.",
+    "eg.",
+    "eig.",
+    "ekskl.",
+    "el.",
+    "et.",
+    "etc.",
+    "etg.",
+    "ev.",
+    "evt.",
+    "f.",
+    "f.Kr.",
+    "f.eks.",
+    "f.o.m.",
+    "fhv.",
+    "fk.",
+    "foreg.",
+    "fork.",
+    "fv.",
+    "fvt.",
+    "g.",
+    "gl.",
+    "gno.",
+    "gnr.",
+    "grl.",
+    "gt.",
+    "h.r.adv.",
+    "hhv.",
+    "hoh.",
+    "hr.",
+    "ifb.",
+    "ifm.",
+    "iht.",
+    "inkl.",
+    "istf.",
+    "jf.",
+    "jr.",
+    "jul.",
+    "juris.",
+    "kfr.",
+    "kgl.",
+    "kgl.res.",
+    "kl.",
+    "komm.",
+    "kr.",
+    "kst.",
+    "lat.",
+    "lø.",
+    "m.a.",
+    "m.a.o.",
+    "m.fl.",
+    "m.m.",
+    "m.v.",
+    "ma.",
+    "mag.art.",
+    "md.",
+    "mfl.",
+    "mht.",
+    "mill.",
+    "min.",
+    "mnd.",
+    "moh.",
+    "mrd.",
+    "muh.",
+    "mv.",
+    "mva.",
+    "n.å.",
+    "ndf.",
+    "nr.",
+    "nto.",
+    "nyno.",
+    "o.a.",
+    "o.l.",
+    "obl.",
+    "off.",
+    "ofl.",
+    "on.",
+    "op.",
+    "org.",
+    "osv.",
+    "ovf.",
+    "p.",
+    "p.a.",
+    "p.g.a.",
+    "p.m.",
+    "p.t.",
+    "pga.",
+    "ph.d.",
+    "pkt.",
+    "pr.",
+    "pst.",
+    "pt.",
+    "red.anm.",
+    "ref.",
+    "res.",
+    "res.kap.",
+    "resp.",
+    "rv.",
+    "s.",
+    "s.d.",
+    "s.k.",
+    "s.u.",
+    "s.å.",
+    "sen.",
+    "sep.",
+    "siviling.",
+    "sms.",
+    "snr.",
+    "spm.",
+    "sr.",
+    "sst.",
+    "st.",
+    "st.meld.",
+    "st.prp.",
+    "stip.",
+    "stk.",
+    "stud.",
+    "sv.",
+    "såk.",
+    "sø.",
+    "t.d.",
+    "t.h.",
+    "t.o.m.",
+    "t.v.",
+    "temp.",
+    "ti.",
+    "tils.",
+    "tilsv.",
+    "tl;dr",
+    "tlf.",
+    "to.",
+    "ult.",
+    "utg.",
+    "v.",
+    "vedk.",
+    "vedr.",
+    "vg.",
+    "vgs.",
+    "vha.",
+    "vit.ass.",
+    "vn.",
+    "vol.",
+    "vs.",
+    "vsa.",
+    "§§",
+    "©NTB",
+    "årg.",
+    "årh.",
+]:
+    _exc[orth] = [{ORTH: orth}]
+
+# Dates
+for h in range(1, 31 + 1):
+    for period in ["."]:
+        _exc[f"{h}{period}"] = [{ORTH: f"{h}."}]
+
+_custom_base_exc = {"i.": [{ORTH: "i", NORM: "i"}, {ORTH: "."}]}
+_exc.update(_custom_base_exc)
+
+TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)

spacy/lang/th/__init__.py

@@ -13,7 +13,6 @@ DEFAULT_CONFIG = """
 """
 
 
-@registry.tokenizers("spacy.th.ThaiTokenizer")
 def create_thai_tokenizer():
     def thai_tokenizer_factory(nlp):
         return ThaiTokenizer(nlp.vocab)

spacy/lang/vi/__init__.py

@@ -22,7 +22,6 @@ use_pyvi = true
 """
 
 
-@registry.tokenizers("spacy.vi.VietnameseTokenizer")
 def create_vietnamese_tokenizer(use_pyvi: bool = True):
     def vietnamese_tokenizer_factory(nlp):
         return VietnameseTokenizer(nlp.vocab, use_pyvi=use_pyvi)

spacy/lang/zh/__init__.py

@@ -46,7 +46,6 @@ class Segmenter(str, Enum):
         return list(cls.__members__.keys())
 
 
-@registry.tokenizers("spacy.zh.ChineseTokenizer")
 def create_chinese_tokenizer(segmenter: Segmenter = Segmenter.char):
     def chinese_tokenizer_factory(nlp):
         return ChineseTokenizer(nlp.vocab, segmenter=segmenter)

spacy/language.py

@@ -5,7 +5,7 @@ import multiprocessing as mp
 import random
 import traceback
 import warnings
-from contextlib import contextmanager
+from contextlib import ExitStack, contextmanager
 from copy import deepcopy
 from dataclasses import dataclass
 from itertools import chain, cycle
@@ -30,8 +30,11 @@ from typing import (
     overload,
 )
 
+import numpy
 import srsly
+from cymem.cymem import Pool
 from thinc.api import Config, CupyOps, Optimizer, get_current_ops
+from thinc.util import convert_recursive
 
 from . import about, ty, util
 from .compat import Literal
@@ -101,7 +104,6 @@ class BaseDefaults:
     writing_system = {"direction": "ltr", "has_case": True, "has_letters": True}
 
 
-@registry.tokenizers("spacy.Tokenizer.v1")
 def create_tokenizer() -> Callable[["Language"], Tokenizer]:
     """Registered function to create a tokenizer. Returns a factory that takes
     the nlp object and returns a Tokenizer instance using the language detaults.
@@ -127,7 +129,6 @@ def create_tokenizer() -> Callable[["Language"], Tokenizer]:
     return tokenizer_factory
 
 
-@registry.misc("spacy.LookupsDataLoader.v1")
 def load_lookups_data(lang, tables):
     util.logger.debug("Loading lookups from spacy-lookups-data: %s", tables)
     lookups = load_lookups(lang=lang, tables=tables)
@@ -140,7 +141,7 @@ class Language:
 
     Defaults (class): Settings, data and factory methods for creating the `nlp`
         object and processing pipeline.
-    lang (str): IETF language code, such as 'en'.
+    lang (str): Two-letter ISO 639-1 or three-letter ISO 639-3 language codes, such as 'en' and 'eng'.
 
     DOCS: https://spacy.io/api/language
     """
@@ -182,6 +183,9 @@ class Language:
 
         DOCS: https://spacy.io/api/language#init
         """
+        from .pipeline.factories import register_factories
+
+        register_factories()
         # We're only calling this to import all factories provided via entry
         # points. The factory decorator applied to these functions takes care
         # of the rest.
@@ -1211,7 +1215,7 @@ class Language:
                     examples,
                 ):
                     eg.predicted = doc
-        return losses
+        return _replace_numpy_floats(losses)
 
     def rehearse(
         self,
@@ -1462,7 +1466,7 @@ class Language:
         results = scorer.score(examples, per_component=per_component)
         n_words = sum(len(eg.predicted) for eg in examples)
         results["speed"] = n_words / (end_time - start_time)
-        return results
+        return _replace_numpy_floats(results)
 
     def create_optimizer(self):
         """Create an optimizer, usually using the [training.optimizer] config."""
@@ -1683,6 +1687,12 @@ class Language:
         for proc in procs:
             proc.start()
 
+        # Close writing-end of channels. This is needed to avoid that reading
+        # from the channel blocks indefinitely when the worker closes the
+        # channel.
+        for tx in bytedocs_send_ch:
+            tx.close()
+
         # Cycle channels not to break the order of docs.
         # The received object is a batch of byte-encoded docs, so flatten them with chain.from_iterable.
         byte_tuples = chain.from_iterable(
@@ -1705,8 +1715,27 @@ class Language:
                     # tell `sender` that one batch was consumed.
                     sender.step()
         finally:
+            # If we are stopping in an orderly fashion, the workers' queues
+            # are empty. Put the sentinel in their queues to signal that work
+            # 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.
+            # To avoid doing unnecessary work or hanging on platforms that
+            # block on sending (Windows), we'll close our end of the channel.
+            # This signals to the worker that it can exit the next time it
+            # attempts to send data down the channel.
+            for r in bytedocs_recv_ch:
+                r.close()
+
             for proc in procs:
-                proc.terminate()
+                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
@@ -2066,6 +2095,38 @@ class Language:
                 util.replace_model_node(pipe.model, listener, new_model)  # type: ignore[attr-defined]
                 tok2vec.remove_listener(listener, pipe_name)
 
+    @contextmanager
+    def memory_zone(self, mem: Optional[Pool] = None) -> Iterator[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.
+
+        Example
+        -------
+        >>> with nlp.memory_zone():
+        ...     for doc in nlp.pipe(texts):
+        ...        process_my_doc(doc)
+        >>> # use_doc(doc) <-- Invalid: doc was allocated in the memory zone
+        """
+        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.vocab.memory_zone(mem))]
+            if hasattr(self.tokenizer, "memory_zone"):
+                contexts.append(stack.enter_context(self.tokenizer.memory_zone(mem)))
+            for _, pipe in self.pipeline:
+                if hasattr(pipe, "memory_zone"):
+                    contexts.append(stack.enter_context(pipe.memory_zone(mem)))
+            yield mem
+
     def to_disk(
         self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
     ) -> None:
@@ -2083,7 +2144,9 @@ class Language:
         serializers["tokenizer"] = lambda p: self.tokenizer.to_disk(  # type: ignore[union-attr]
             p, exclude=["vocab"]
         )
-        serializers["meta.json"] = lambda p: srsly.write_json(p, self.meta)
+        serializers["meta.json"] = lambda p: srsly.write_json(
+            p, _replace_numpy_floats(self.meta)
+        )
         serializers["config.cfg"] = lambda p: self.config.to_disk(p)
         for name, proc in self._components:
             if name in exclude:
@@ -2197,7 +2260,9 @@ class Language:
         serializers: Dict[str, Callable[[], bytes]] = {}
         serializers["vocab"] = lambda: self.vocab.to_bytes(exclude=exclude)
         serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"])  # type: ignore[union-attr]
-        serializers["meta.json"] = lambda: srsly.json_dumps(self.meta)
+        serializers["meta.json"] = lambda: srsly.json_dumps(
+            _replace_numpy_floats(self.meta)
+        )
         serializers["config.cfg"] = lambda: self.config.to_bytes()
         for name, proc in self._components:
             if name in exclude:
@@ -2248,6 +2313,12 @@ class Language:
         return self
 
 
+def _replace_numpy_floats(meta_dict: dict) -> dict:
+    return convert_recursive(
+        lambda v: isinstance(v, numpy.floating), lambda v: float(v), dict(meta_dict)
+    )
+
+
 @dataclass
 class FactoryMeta:
     """Dataclass containing information about a component and its defaults
@@ -2323,6 +2394,13 @@ def _apply_pipes(
     while True:
         try:
             texts_with_ctx = receiver.get()
+
+            # Stop working if we encounter the end-of-work sentinel.
+            if isinstance(texts_with_ctx, _WorkDoneSentinel):
+                sender.close()
+                receiver.close()
+                return
+
             docs = (
                 ensure_doc(doc_like, context) for doc_like, context in texts_with_ctx
             )
@@ -2331,11 +2409,23 @@ def _apply_pipes(
             # Connection does not accept unpickable objects, so send list.
             byte_docs = [(doc.to_bytes(), doc._context, None) for doc in docs]
             padding = [(None, None, None)] * (len(texts_with_ctx) - len(byte_docs))
-            sender.send(byte_docs + padding)  # type: ignore[operator]
+            data: Sequence[Tuple[Optional[bytes], Optional[Any], Optional[bytes]]] = (
+                byte_docs + padding  # type: ignore[operator]
+            )
         except Exception:
             error_msg = [(None, None, srsly.msgpack_dumps(traceback.format_exc()))]
             padding = [(None, None, None)] * (len(texts_with_ctx) - 1)
-            sender.send(error_msg + padding)
+            data = error_msg + padding
+
+        try:
+            sender.send(data)
+        except BrokenPipeError:
+            # 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
 
 
 class _Sender:
@@ -2365,3 +2455,10 @@ class _Sender:
         if self.count >= self.chunk_size:
             self.count = 0
             self.send()
+
+
+class _WorkDoneSentinel:
+    pass
+
+
+_WORK_DONE_SENTINEL = _WorkDoneSentinel()

spacy/lexeme.pxd

@@ -35,7 +35,7 @@ cdef class Lexeme:
         return self
 
     @staticmethod
-    cdef inline void set_struct_attr(LexemeC* lex, attr_id_t name, attr_t value) nogil:
+    cdef inline void set_struct_attr(LexemeC* lex, attr_id_t name, attr_t value) noexcept nogil:
         if name < (sizeof(flags_t) * 8):
             Lexeme.c_set_flag(lex, name, value)
         elif name == ID:
@@ -54,7 +54,7 @@ cdef class Lexeme:
             lex.lang = value
 
     @staticmethod
-    cdef inline attr_t get_struct_attr(const LexemeC* lex, attr_id_t feat_name) nogil:
+    cdef inline attr_t get_struct_attr(const LexemeC* lex, attr_id_t feat_name) noexcept nogil:
         if feat_name < (sizeof(flags_t) * 8):
             if Lexeme.c_check_flag(lex, feat_name):
                 return 1
@@ -82,7 +82,7 @@ cdef class Lexeme:
             return 0
 
     @staticmethod
-    cdef inline bint c_check_flag(const LexemeC* lexeme, attr_id_t flag_id) nogil:
+    cdef inline bint c_check_flag(const LexemeC* lexeme, attr_id_t flag_id) noexcept nogil:
         cdef flags_t one = 1
         if lexeme.flags & (one << flag_id):
             return True
@@ -90,7 +90,7 @@ cdef class Lexeme:
             return False
 
     @staticmethod
-    cdef inline bint c_set_flag(LexemeC* lex, attr_id_t flag_id, bint value) nogil:
+    cdef inline bint c_set_flag(LexemeC* lex, attr_id_t flag_id, bint value) noexcept nogil:
         cdef flags_t one = 1
         if value:
             lex.flags |= one << flag_id

spacy/lexeme.pyx

@@ -70,7 +70,7 @@ cdef class Lexeme:
         if isinstance(other, Lexeme):
             a = self.orth
             b = other.orth
-        elif isinstance(other, long):
+        elif isinstance(other, int):
             a = self.orth
             b = other
         elif isinstance(other, str):
@@ -104,7 +104,7 @@ cdef class Lexeme:
             # skip PROB, e.g. from lexemes.jsonl
             if isinstance(value, float):
                 continue
-            elif isinstance(value, (int, long)):
+            elif isinstance(value, int):
                 Lexeme.set_struct_attr(self.c, attr, value)
             else:
                 Lexeme.set_struct_attr(self.c, attr, self.vocab.strings.add(value))
@@ -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
-            cdef int length = self.vocab.vectors_length
+        if length == 0:
-            if length == 0:
+            raise ValueError(Errors.E010)
-                raise ValueError(Errors.E010)
+        return self.vocab.get_vector(self.c.orth)
-            return self.vocab.get_vector(self.c.orth)
 
-        def __set__(self, vector):
+    @vector.setter
-            if len(vector) != self.vocab.vectors_length:
+    def vector(self, vector):
-                raise ValueError(Errors.E073.format(new_length=len(vector),
+        if len(vector) != self.vocab.vectors_length:
-                                                    length=self.vocab.vectors_length))
+            raise ValueError(Errors.E073.format(new_length=len(vector),
-            self.vocab.set_vector(self.c.orth, 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):
+    @rank.setter
-            self.c.id = value
+    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", {})
-            sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment", {})
+        return sentiment_table.get(self.c.orth, 0.0)
-            return sentiment_table.get(self.c.orth, 0.0)
 
-        def __set__(self, float x):
+    @sentiment.setter
-            if "lexeme_sentiment" not in self.vocab.lookups:
+    def sentiment(self, float x):
-                self.vocab.lookups.add_table("lexeme_sentiment")
+        if "lexeme_sentiment" not in self.vocab.lookups:
-            sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment")
+            self.vocab.lookups.add_table("lexeme_sentiment")
-            sentiment_table[self.c.orth] = x
+        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):
+    @lower.setter
-            self.c.lower = x
+    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):
+    @norm.setter
-            if "lexeme_norm" not in self.vocab.lookups:
+    def norm(self, attr_t x):
-                self.vocab.lookups.add_table("lexeme_norm")
+        if "lexeme_norm" not in self.vocab.lookups:
-            norm_table = self.vocab.lookups.get_table("lexeme_norm")
+            self.vocab.lookups.add_table("lexeme_norm")
-            norm_table[self.c.orth] = self.vocab.strings[x]
+        norm_table = self.vocab.lookups.get_table("lexeme_norm")
-            self.c.norm = x
+        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):
+    @shape.setter
-            self.c.shape = x
+    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):
+    @prefix.setter
-            self.c.prefix = x
+    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):
+    @suffix.setter
-            self.c.suffix = x
+    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", {})
-            cluster_table = self.vocab.lookups.get_table("lexeme_cluster", {})
+        return cluster_table.get(self.c.orth, 0)
-            return cluster_table.get(self.c.orth, 0)
 
-        def __set__(self, int x):
+    @cluster.setter
-            cluster_table = self.vocab.lookups.get_table("lexeme_cluster", {})
+    def cluster(self, int x):
-            cluster_table[self.c.orth] = 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):
+    @lang.setter
-            self.c.lang = x
+    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", {})
-            prob_table = self.vocab.lookups.get_table("lexeme_prob", {})
+        settings_table = self.vocab.lookups.get_table("lexeme_settings", {})
-            settings_table = self.vocab.lookups.get_table("lexeme_settings", {})
+        default_oov_prob = settings_table.get("oov_prob", -20.0)
-            default_oov_prob = settings_table.get("oov_prob", -20.0)
+        return prob_table.get(self.c.orth, default_oov_prob)
-            return prob_table.get(self.c.orth, default_oov_prob)
 
-        def __set__(self, float x):
+    @prob.setter
-            prob_table = self.vocab.lookups.get_table("lexeme_prob", {})
+    def prob(self, float x):
-            prob_table[self.c.orth] = 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):
+    @lower_.setter
-            self.c.lower = self.vocab.strings.add(x)
+    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):
+    @norm_.setter
-            self.norm = self.vocab.strings.add(x)
+    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):
+    @shape_.setter
-            self.c.shape = self.vocab.strings.add(x)
+    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):
+    @prefix_.setter
-            self.c.prefix = self.vocab.strings.add(x)
+    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):
+    @suffix_.setter
-            self.c.suffix = self.vocab.strings.add(x)
+    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):
+    @lang_.setter
-            self.c.lang = self.vocab.strings.add(x)
+    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):
+    @flags.setter
-            self.c.flags = x
+    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):
+    @is_stop.setter
-            Lexeme.c_set_flag(self.c, IS_STOP, x)
+    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):
+    @is_alpha.setter
-            Lexeme.c_set_flag(self.c, IS_ALPHA, x)
+    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):
+    @is_ascii.setter
-            Lexeme.c_set_flag(self.c, IS_ASCII, x)
+    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):
+    @is_digit.setter
-            Lexeme.c_set_flag(self.c, IS_DIGIT, x)
+    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):
+    @is_lower.setter
-            Lexeme.c_set_flag(self.c, IS_LOWER, x)
+    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):
+    @is_upper.setter
-            Lexeme.c_set_flag(self.c, IS_UPPER, x)
+    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):
+    @is_title.setter
-            Lexeme.c_set_flag(self.c, IS_TITLE, x)
+    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):
+    @is_punct.setter
-            Lexeme.c_set_flag(self.c, IS_PUNCT, x)
+    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):
+    @is_space.setter
-            Lexeme.c_set_flag(self.c, IS_SPACE, x)
+    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):
+    @is_bracket.setter
-            Lexeme.c_set_flag(self.c, IS_BRACKET, x)
+    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):
+    @is_quote.setter
-            Lexeme.c_set_flag(self.c, IS_QUOTE, x)
+    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):
+    @is_left_punct.setter
-            Lexeme.c_set_flag(self.c, IS_LEFT_PUNCT, x)
+    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):
+    @is_right_punct.setter
-            Lexeme.c_set_flag(self.c, IS_RIGHT_PUNCT, x)
+    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):
+    @is_currency.setter
-            Lexeme.c_set_flag(self.c, IS_CURRENCY, x)
+    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):
+    @like_url.setter
-            Lexeme.c_set_flag(self.c, LIKE_URL, x)
+    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):
+    @like_num.setter
-            Lexeme.c_set_flag(self.c, LIKE_NUM, x)
+    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):
+    @like_email.setter
-            Lexeme.c_set_flag(self.c, LIKE_EMAIL, x)
+    def like_email(self, bint x):
+        Lexeme.c_set_flag(self.c, LIKE_EMAIL, x)

spacy/matcher/levenshtein.pyx

@@ -1,4 +1,4 @@
-# cython: binding=True, infer_types=True
+# cython: binding=True, infer_types=True, language_level=3
 from cpython.object cimport PyObject
 from libc.stdint cimport int64_t
 
@@ -27,6 +27,5 @@ cpdef bint levenshtein_compare(input_text: str, pattern_text: str, fuzzy: int =
     return levenshtein(input_text, pattern_text, max_edits) <= max_edits
 
 
-@registry.misc("spacy.levenshtein_compare.v1")
 def make_levenshtein_compare():
     return levenshtein_compare

spacy/matcher/matcher.pyx

@@ -625,7 +625,7 @@ cdef action_t get_action(
     const TokenC * token,
     const attr_t * extra_attrs,
     const int8_t * predicate_matches
-) nogil:
+) noexcept nogil:
     """We need to consider:
     a) Does the token match the specification? [Yes, No]
     b) What's the quantifier? [1, 0+, ?]
@@ -740,7 +740,7 @@ cdef int8_t get_is_match(
     const TokenC* token,
     const attr_t* extra_attrs,
     const int8_t* predicate_matches
-) nogil:
+) noexcept nogil:
     for i in range(state.pattern.nr_py):
         if predicate_matches[state.pattern.py_predicates[i]] == -1:
             return 0
@@ -755,14 +755,14 @@ cdef int8_t get_is_match(
     return True
 
 
-cdef inline int8_t get_is_final(PatternStateC state) nogil:
+cdef inline int8_t get_is_final(PatternStateC state) noexcept nogil:
     if state.pattern[1].quantifier == FINAL_ID:
         return 1
     else:
         return 0
 
 
-cdef inline int8_t get_quantifier(PatternStateC state) nogil:
+cdef inline int8_t get_quantifier(PatternStateC state) noexcept nogil:
     return state.pattern.quantifier
 
 
@@ -805,7 +805,7 @@ cdef TokenPatternC* init_pattern(Pool mem, attr_t entity_id, object token_specs)
     return pattern
 
 
-cdef attr_t get_ent_id(const TokenPatternC* pattern) nogil:
+cdef attr_t get_ent_id(const TokenPatternC* pattern) noexcept nogil:
     while pattern.quantifier != FINAL_ID:
         pattern += 1
     id_attr = pattern[0].attrs[0]

spacy/matcher/phrasematcher.pyx

@@ -47,7 +47,7 @@ cdef class PhraseMatcher:
         self._terminal_hash = 826361138722620965
         map_init(self.mem, self.c_map, 8)
 
-        if isinstance(attr, (int, long)):
+        if isinstance(attr, int):
             self.attr = attr
         else:
             if attr is None:

spacy/ml/_character_embed.py

@@ -7,7 +7,6 @@ from ..tokens import Doc
 from ..util import registry
 
 
-@registry.layers("spacy.CharEmbed.v1")
 def CharacterEmbed(nM: int, nC: int) -> Model[List[Doc], List[Floats2d]]:
     # nM: Number of dimensions per character. nC: Number of characters.
     return Model(

spacy/ml/_precomputable_affine.py

@@ -3,7 +3,6 @@ from thinc.api import Model, normal_init
 from ..util import registry
 
 
-@registry.layers("spacy.PrecomputableAffine.v1")
 def PrecomputableAffine(nO, nI, nF, nP, dropout=0.1):
     model = Model(
         "precomputable_affine",

spacy/ml/callbacks.py

@@ -50,7 +50,6 @@ def models_with_nvtx_range(nlp, forward_color: int, backprop_color: int):
     return nlp
 
 
-@registry.callbacks("spacy.models_with_nvtx_range.v1")
 def create_models_with_nvtx_range(
     forward_color: int = -1, backprop_color: int = -1
 ) -> Callable[["Language"], "Language"]:
@@ -110,7 +109,6 @@ def pipes_with_nvtx_range(
     return nlp
 
 
-@registry.callbacks("spacy.models_and_pipes_with_nvtx_range.v1")
 def create_models_and_pipes_with_nvtx_range(
     forward_color: int = -1,
     backprop_color: int = -1,

spacy/ml/extract_ngrams.py

@@ -4,7 +4,6 @@ from ..attrs import LOWER
 from ..util import registry
 
 
-@registry.layers("spacy.extract_ngrams.v1")
 def extract_ngrams(ngram_size: int, attr: int = LOWER) -> Model:
     model: Model = Model("extract_ngrams", forward)
     model.attrs["ngram_size"] = ngram_size

spacy/ml/extract_spans.py

@@ -6,7 +6,6 @@ from thinc.types import Ints1d, Ragged
 from ..util import registry
 
 
-@registry.layers("spacy.extract_spans.v1")
 def extract_spans() -> Model[Tuple[Ragged, Ragged], Ragged]:
     """Extract spans from a sequence of source arrays, as specified by an array
     of (start, end) indices. The output is a ragged array of the

spacy/ml/featureextractor.py

@@ -6,8 +6,9 @@ from thinc.types import Ints2d
 from ..tokens import Doc
 
 
-@registry.layers("spacy.FeatureExtractor.v1")
+def FeatureExtractor(
-def FeatureExtractor(columns: List[Union[int, str]]) -> Model[List[Doc], List[Ints2d]]:
+    columns: Union[List[str], List[int], List[Union[int, str]]]
+) -> Model[List[Doc], List[Ints2d]]:
     return Model("extract_features", forward, attrs={"columns": columns})
 
 

spacy/ml/models/entity_linker.py

@@ -28,7 +28,6 @@ from ...vocab import Vocab
 from ..extract_spans import extract_spans
 
 
-@registry.architectures("spacy.EntityLinker.v2")
 def build_nel_encoder(
     tok2vec: Model, nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
@@ -92,7 +91,6 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
     return out, lambda x: []
 
 
-@registry.misc("spacy.KBFromFile.v1")
 def load_kb(
     kb_path: Path,
 ) -> Callable[[Vocab], KnowledgeBase]:
@@ -104,7 +102,6 @@ def load_kb(
     return kb_from_file
 
 
-@registry.misc("spacy.EmptyKB.v2")
 def empty_kb_for_config() -> Callable[[Vocab, int], KnowledgeBase]:
     def empty_kb_factory(vocab: Vocab, entity_vector_length: int):
         return InMemoryLookupKB(vocab=vocab, entity_vector_length=entity_vector_length)
@@ -112,7 +109,6 @@ def empty_kb_for_config() -> Callable[[Vocab, int], KnowledgeBase]:
     return empty_kb_factory
 
 
-@registry.misc("spacy.EmptyKB.v1")
 def empty_kb(
     entity_vector_length: int,
 ) -> Callable[[Vocab], KnowledgeBase]:
@@ -122,12 +118,10 @@ def empty_kb(
     return empty_kb_factory
 
 
-@registry.misc("spacy.CandidateGenerator.v1")
 def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
     return get_candidates
 
 
-@registry.misc("spacy.CandidateBatchGenerator.v1")
 def create_candidates_batch() -> Callable[
     [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
 ]:

spacy/ml/models/multi_task.py

@@ -30,7 +30,6 @@ if TYPE_CHECKING:
     from ...vocab import Vocab  # noqa: F401
 
 
-@registry.architectures("spacy.PretrainVectors.v1")
 def create_pretrain_vectors(
     maxout_pieces: int, hidden_size: int, loss: str
 ) -> Callable[["Vocab", Model], Model]:
@@ -57,7 +56,6 @@ def create_pretrain_vectors(
     return create_vectors_objective
 
 
-@registry.architectures("spacy.PretrainCharacters.v1")
 def create_pretrain_characters(
     maxout_pieces: int, hidden_size: int, n_characters: int
 ) -> Callable[["Vocab", Model], Model]:

spacy/ml/models/parser.py

@@ -11,7 +11,6 @@ from .._precomputable_affine import PrecomputableAffine
 from ..tb_framework import TransitionModel
 
 
-@registry.architectures("spacy.TransitionBasedParser.v2")
 def build_tb_parser_model(
     tok2vec: Model[List[Doc], List[Floats2d]],
     state_type: Literal["parser", "ner"],

spacy/ml/models/span_finder.py

@@ -10,7 +10,6 @@ InT = List[Doc]
 OutT = Floats2d
 
 
-@registry.architectures("spacy.SpanFinder.v1")
 def build_finder_model(
     tok2vec: Model[InT, List[Floats2d]], scorer: Model[OutT, OutT]
 ) -> Model[InT, OutT]:

spacy/ml/models/spancat.py

@@ -22,7 +22,6 @@ from ...util import registry
 from ..extract_spans import extract_spans
 
 
-@registry.layers("spacy.LinearLogistic.v1")
 def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
     """An output layer for multi-label classification. It uses a linear layer
     followed by a logistic activation.
@@ -30,7 +29,6 @@ def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
     return chain(Linear(nO=nO, nI=nI, init_W=glorot_uniform_init), Logistic())
 
 
-@registry.layers("spacy.mean_max_reducer.v1")
 def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
     """Reduce sequences by concatenating their mean and max pooled vectors,
     and then combine the concatenated vectors with a hidden layer.
@@ -46,7 +44,6 @@ def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
     )
 
 
-@registry.architectures("spacy.SpanCategorizer.v1")
 def build_spancat_model(
     tok2vec: Model[List[Doc], List[Floats2d]],
     reducer: Model[Ragged, Floats2d],

spacy/ml/models/tagger.py

@@ -7,7 +7,6 @@ from ...tokens import Doc
 from ...util import registry
 
 
-@registry.architectures("spacy.Tagger.v2")
 def build_tagger_model(
     tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None, normalize=False
 ) -> Model[List[Doc], List[Floats2d]]:

spacy/ml/models/textcat.py

@@ -1,21 +1,27 @@
 from functools import partial
-from typing import List, Optional, cast
+from typing import List, Optional, Tuple, cast
 
 from thinc.api import (
     Dropout,
+    Gelu,
     LayerNorm,
     Linear,
     Logistic,
     Maxout,
     Model,
     ParametricAttention,
+    ParametricAttention_v2,
     Relu,
     Softmax,
     SparseLinear,
+    SparseLinear_v2,
     chain,
     clone,
     concatenate,
     list2ragged,
+    reduce_first,
+    reduce_last,
+    reduce_max,
     reduce_mean,
     reduce_sum,
     residual,
@@ -25,9 +31,10 @@ from thinc.api import (
 )
 from thinc.layers.chain import init as init_chain
 from thinc.layers.resizable import resize_linear_weighted, resize_model
-from thinc.types import Floats2d
+from thinc.types import ArrayXd, Floats2d
 
 from ...attrs import ORTH
+from ...errors import Errors
 from ...tokens import Doc
 from ...util import registry
 from ..extract_ngrams import extract_ngrams
@@ -37,7 +44,6 @@ from .tok2vec import get_tok2vec_width
 NEG_VALUE = -5000
 
 
-@registry.architectures("spacy.TextCatCNN.v2")
 def build_simple_cnn_text_classifier(
     tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
@@ -47,39 +53,15 @@ def build_simple_cnn_text_classifier(
     outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
     is applied instead, so that outputs are in the range [0, 1].
     """
-    fill_defaults = {"b": 0, "W": 0}
+    return build_reduce_text_classifier(
-    with Model.define_operators({">>": chain}):
+        tok2vec=tok2vec,
-        cnn = tok2vec >> list2ragged() >> reduce_mean()
+        exclusive_classes=exclusive_classes,
-        nI = tok2vec.maybe_get_dim("nO")
+        use_reduce_first=False,
-        if exclusive_classes:
+        use_reduce_last=False,
-            output_layer = Softmax(nO=nO, nI=nI)
+        use_reduce_max=False,
-            fill_defaults["b"] = NEG_VALUE
+        use_reduce_mean=True,
-            resizable_layer: Model = resizable(
+        nO=nO,
-                output_layer,
+    )
-                resize_layer=partial(
-                    resize_linear_weighted, fill_defaults=fill_defaults
-                ),
-            )
-            model = cnn >> resizable_layer
-        else:
-            output_layer = Linear(nO=nO, nI=nI)
-            resizable_layer = resizable(
-                output_layer,
-                resize_layer=partial(
-                    resize_linear_weighted, fill_defaults=fill_defaults
-                ),
-            )
-            model = cnn >> resizable_layer >> Logistic()
-        model.set_ref("output_layer", output_layer)
-        model.attrs["resize_output"] = partial(
-            resize_and_set_ref,
-            resizable_layer=resizable_layer,
-        )
-    model.set_ref("tok2vec", tok2vec)
-    if nO is not None:
-        model.set_dim("nO", cast(int, nO))
-    model.attrs["multi_label"] = not exclusive_classes
-    return model
 
 
 def resize_and_set_ref(model, new_nO, resizable_layer):
@@ -89,16 +71,52 @@ def resize_and_set_ref(model, new_nO, resizable_layer):
     return model
 
 
-@registry.architectures("spacy.TextCatBOW.v2")
 def build_bow_text_classifier(
     exclusive_classes: bool,
     ngram_size: int,
     no_output_layer: bool,
     nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    return _build_bow_text_classifier(
+        exclusive_classes=exclusive_classes,
+        ngram_size=ngram_size,
+        no_output_layer=no_output_layer,
+        nO=nO,
+        sparse_linear=SparseLinear(nO=nO),
+    )
+
+
+def build_bow_text_classifier_v3(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    length: int = 262144,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    if length < 1:
+        raise ValueError(Errors.E1056.format(length=length))
+
+    # Find k such that 2**(k-1) < length <= 2**k.
+    length = 2 ** (length - 1).bit_length()
+
+    return _build_bow_text_classifier(
+        exclusive_classes=exclusive_classes,
+        ngram_size=ngram_size,
+        no_output_layer=no_output_layer,
+        nO=nO,
+        sparse_linear=SparseLinear_v2(nO=nO, length=length),
+    )
+
+
+def _build_bow_text_classifier(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    sparse_linear: Model[Tuple[ArrayXd, ArrayXd, ArrayXd], ArrayXd],
+    nO: Optional[int] = None,
 ) -> Model[List[Doc], Floats2d]:
     fill_defaults = {"b": 0, "W": 0}
     with Model.define_operators({">>": chain}):
-        sparse_linear = SparseLinear(nO=nO)
         output_layer = None
         if not no_output_layer:
             fill_defaults["b"] = NEG_VALUE
@@ -121,12 +139,14 @@ def build_bow_text_classifier(
     return model
 
 
-@registry.architectures("spacy.TextCatEnsemble.v2")
 def build_text_classifier_v2(
     tok2vec: Model[List[Doc], List[Floats2d]],
     linear_model: Model[List[Doc], Floats2d],
     nO: Optional[int] = None,
 ) -> Model[List[Doc], Floats2d]:
+    # TODO: build the model with _build_parametric_attention_with_residual_nonlinear
+    # in spaCy v4. We don't do this in spaCy v3 to preserve model
+    # compatibility.
     exclusive_classes = not linear_model.attrs["multi_label"]
     with Model.define_operators({">>": chain, "|": concatenate}):
         width = tok2vec.maybe_get_dim("nO")
@@ -161,6 +181,11 @@ 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)
@@ -171,7 +196,6 @@ def init_ensemble_textcat(model, X, Y) -> Model:
     return model
 
 
-@registry.architectures("spacy.TextCatLowData.v1")
 def build_text_classifier_lowdata(
     width: int, dropout: Optional[float], nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
@@ -190,3 +214,151 @@ def build_text_classifier_lowdata(
             model = model >> Dropout(dropout)
         model = model >> Logistic()
     return model
+
+
+def build_textcat_parametric_attention_v1(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    exclusive_classes: bool,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    width = tok2vec.maybe_get_dim("nO")
+    parametric_attention = _build_parametric_attention_with_residual_nonlinear(
+        tok2vec=tok2vec,
+        nonlinear_layer=Maxout(nI=width, nO=width),
+        key_transform=Gelu(nI=width, nO=width),
+    )
+    with Model.define_operators({">>": chain}):
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO)
+        else:
+            output_layer = Linear(nO=nO) >> Logistic()
+        model = parametric_attention >> output_layer
+    if model.has_dim("nO") is not False and nO is not None:
+        model.set_dim("nO", cast(int, nO))
+    model.set_ref("output_layer", output_layer)
+    model.attrs["multi_label"] = not exclusive_classes
+
+    return model
+
+
+def _build_parametric_attention_with_residual_nonlinear(
+    *,
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    nonlinear_layer: Model[Floats2d, Floats2d],
+    key_transform: Optional[Model[Floats2d, Floats2d]] = None,
+) -> Model[List[Doc], Floats2d]:
+    with Model.define_operators({">>": chain, "|": concatenate}):
+        width = tok2vec.maybe_get_dim("nO")
+        attention_layer = ParametricAttention_v2(nO=width, key_transform=key_transform)
+        norm_layer = LayerNorm(nI=width)
+        parametric_attention = (
+            tok2vec
+            >> list2ragged()
+            >> attention_layer
+            >> reduce_sum()
+            >> residual(nonlinear_layer >> norm_layer >> Dropout(0.0))
+        )
+
+        parametric_attention.init = _init_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)
+
+        return parametric_attention
+
+
+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("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)
+    return model
+
+
+def build_reduce_text_classifier(
+    tok2vec: Model,
+    exclusive_classes: bool,
+    use_reduce_first: bool,
+    use_reduce_last: bool,
+    use_reduce_max: bool,
+    use_reduce_mean: bool,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    """Build a model that classifies pooled `Doc` representations.
+
+    Pooling is performed using reductions. Reductions are concatenated when
+    multiple reductions are used.
+
+    tok2vec (Model): the tok2vec layer to pool over.
+    exclusive_classes (bool): Whether or not classes are mutually exclusive.
+    use_reduce_first (bool): Pool by using the hidden representation of the
+        first token of a `Doc`.
+    use_reduce_last (bool): Pool by using the hidden representation of the
+        last token of a `Doc`.
+    use_reduce_max (bool): Pool by taking the maximum values of the hidden
+        representations of a `Doc`.
+    use_reduce_mean (bool): Pool by taking the mean of all hidden
+        representations of a `Doc`.
+    nO (Optional[int]): Number of classes.
+    """
+
+    fill_defaults = {"b": 0, "W": 0}
+    reductions = []
+    if use_reduce_first:
+        reductions.append(reduce_first())
+    if use_reduce_last:
+        reductions.append(reduce_last())
+    if use_reduce_max:
+        reductions.append(reduce_max())
+    if use_reduce_mean:
+        reductions.append(reduce_mean())
+
+    if not len(reductions):
+        raise ValueError(Errors.E1057)
+
+    with Model.define_operators({">>": chain}):
+        cnn = tok2vec >> list2ragged() >> concatenate(*reductions)
+        nO_tok2vec = tok2vec.maybe_get_dim("nO")
+        nI = nO_tok2vec * len(reductions) if nO_tok2vec is not None else None
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO, nI=nI)
+            fill_defaults["b"] = NEG_VALUE
+            resizable_layer: Model = resizable(
+                output_layer,
+                resize_layer=partial(
+                    resize_linear_weighted, fill_defaults=fill_defaults
+                ),
+            )
+            model = cnn >> resizable_layer
+        else:
+            output_layer = Linear(nO=nO, nI=nI)
+            resizable_layer = resizable(
+                output_layer,
+                resize_layer=partial(
+                    resize_linear_weighted, fill_defaults=fill_defaults
+                ),
+            )
+            model = cnn >> resizable_layer >> Logistic()
+        model.set_ref("output_layer", output_layer)
+        model.attrs["resize_output"] = partial(
+            resize_and_set_ref,
+            resizable_layer=resizable_layer,
+        )
+    model.set_ref("tok2vec", tok2vec)
+    if nO is not None:
+        model.set_dim("nO", cast(int, nO))
+    model.attrs["multi_label"] = not exclusive_classes
+    return model

spacy/ml/models/tok2vec.py

@@ -29,7 +29,6 @@ from ..featureextractor import FeatureExtractor
 from ..staticvectors import StaticVectors
 
 
-@registry.architectures("spacy.Tok2VecListener.v1")
 def tok2vec_listener_v1(width: int, upstream: str = "*"):
     tok2vec = Tok2VecListener(upstream_name=upstream, width=width)
     return tok2vec
@@ -46,7 +45,6 @@ def get_tok2vec_width(model: Model):
     return nO
 
 
-@registry.architectures("spacy.HashEmbedCNN.v2")
 def build_hash_embed_cnn_tok2vec(
     *,
     width: int,
@@ -102,7 +100,6 @@ def build_hash_embed_cnn_tok2vec(
     )
 
 
-@registry.architectures("spacy.Tok2Vec.v2")
 def build_Tok2Vec_model(
     embed: Model[List[Doc], List[Floats2d]],
     encode: Model[List[Floats2d], List[Floats2d]],
@@ -123,10 +120,9 @@ def build_Tok2Vec_model(
     return tok2vec
 
 
-@registry.architectures("spacy.MultiHashEmbed.v2")
 def MultiHashEmbed(
     width: int,
-    attrs: List[Union[str, int]],
+    attrs: Union[List[str], List[int], List[Union[str, int]]],
     rows: List[int],
     include_static_vectors: bool,
 ) -> Model[List[Doc], List[Floats2d]]:
@@ -192,7 +188,7 @@ def MultiHashEmbed(
         )
     else:
         model = chain(
-            FeatureExtractor(list(attrs)),
+            FeatureExtractor(attrs),
             cast(Model[List[Ints2d], Ragged], list2ragged()),
             with_array(concatenate(*embeddings)),
             max_out,
@@ -201,7 +197,6 @@ def MultiHashEmbed(
     return model
 
 
-@registry.architectures("spacy.CharacterEmbed.v2")
 def CharacterEmbed(
     width: int,
     rows: int,
@@ -278,7 +273,6 @@ def CharacterEmbed(
     return model
 
 
-@registry.architectures("spacy.MaxoutWindowEncoder.v2")
 def MaxoutWindowEncoder(
     width: int, window_size: int, maxout_pieces: int, depth: int
 ) -> Model[List[Floats2d], List[Floats2d]]:
@@ -310,7 +304,6 @@ def MaxoutWindowEncoder(
     return with_array(model, pad=receptive_field)
 
 
-@registry.architectures("spacy.MishWindowEncoder.v2")
 def MishWindowEncoder(
     width: int, window_size: int, depth: int
 ) -> Model[List[Floats2d], List[Floats2d]]:
@@ -333,7 +326,6 @@ def MishWindowEncoder(
     return with_array(model)
 
 
-@registry.architectures("spacy.TorchBiLSTMEncoder.v1")
 def BiLSTMEncoder(
     width: int, depth: int, dropout: float
 ) -> Model[List[Floats2d], List[Floats2d]]:

spacy/ml/parser_model.pyx

@@ -52,14 +52,14 @@ cdef SizesC get_c_sizes(model, int batch_size) except *:
     return output
 
 
-cdef ActivationsC alloc_activations(SizesC n) nogil:
+cdef ActivationsC alloc_activations(SizesC n) noexcept nogil:
     cdef ActivationsC A
     memset(&A, 0, sizeof(A))
     resize_activations(&A, n)
     return A
 
 
-cdef void free_activations(const ActivationsC* A) nogil:
+cdef void free_activations(const ActivationsC* A) noexcept nogil:
     free(A.token_ids)
     free(A.scores)
     free(A.unmaxed)
@@ -67,7 +67,7 @@ cdef void free_activations(const ActivationsC* A) nogil:
     free(A.is_valid)
 
 
-cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
+cdef void resize_activations(ActivationsC* A, SizesC n) noexcept nogil:
     if n.states <= A._max_size:
         A._curr_size = n.states
         return
@@ -100,7 +100,7 @@ cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
 
 cdef void predict_states(
     CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n
-) nogil:
+) noexcept nogil:
     resize_activations(A, n)
     for i in range(n.states):
         states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
@@ -159,7 +159,7 @@ cdef void sum_state_features(
     int B,
     int F,
     int O
-) nogil:
+) noexcept nogil:
     cdef int idx, b, f
     cdef const float* feature
     padding = cached
@@ -183,7 +183,7 @@ cdef void cpu_log_loss(
     const int* is_valid,
     const float* scores,
     int O
-) nogil:
+) noexcept nogil:
     """Do multi-label log loss"""
     cdef double max_, gmax, Z, gZ
     best = arg_max_if_gold(scores, costs, is_valid, O)
@@ -209,7 +209,7 @@ cdef void cpu_log_loss(
 
 cdef int arg_max_if_gold(
     const weight_t* scores, const weight_t* costs, const int* is_valid, int n
-) nogil:
+) noexcept nogil:
     # Find minimum cost
     cdef float cost = 1
     for i in range(n):
@@ -224,7 +224,7 @@ cdef int arg_max_if_gold(
     return best
 
 
-cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil:
+cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) noexcept nogil:
     cdef int best = -1
     for i in range(n):
         if is_valid[i] >= 1:

spacy/ml/staticvectors.py

@@ -13,7 +13,6 @@ from ..vectors import Mode, Vectors
 from ..vocab import Vocab
 
 
-@registry.layers("spacy.StaticVectors.v2")
 def StaticVectors(
     nO: Optional[int] = None,
     nM: Optional[int] = None,

spacy/ml/tb_framework.py

@@ -4,7 +4,6 @@ from ..util import registry
 from .parser_model import ParserStepModel
 
 
-@registry.layers("spacy.TransitionModel.v1")
 def TransitionModel(
     tok2vec, lower, upper, resize_output, dropout=0.2, unseen_classes=set()
 ):

spacy/morphology.pyx

@@ -57,16 +57,20 @@ cdef class Morphology:
         field_feature_pairs = []
         for field in sorted(string_features):
             values = string_features[field]
+            self.strings.add(field, allow_transient=False),
+            field_id = self.strings[field]
             for value in values.split(self.VALUE_SEP):
+                field_sep_value = field + self.FIELD_SEP + value
+                self.strings.add(field_sep_value, allow_transient=False),
                 field_feature_pairs.append((
-                    self.strings.add(field),
+                    field_id,
-                    self.strings.add(field + self.FIELD_SEP + value),
+                    self.strings[field_sep_value]
                 ))
         cdef MorphAnalysisC tag = self.create_morph_tag(field_feature_pairs)
         # the hash key for the tag is either the hash of the normalized UFEATS
         # string or the hash of an empty placeholder
         norm_feats_string = self.normalize_features(features)
-        tag.key = self.strings.add(norm_feats_string)
+        tag.key = self.strings.add(norm_feats_string, allow_transient=False)
         self.insert(tag)
         return tag.key
 

spacy/parts_of_speech.pyx

@@ -25,3 +25,8 @@ IDS = {
 
 
 NAMES = {value: key for key, value in IDS.items()}
+
+# As of Cython 3.1, the global Python namespace no longer has the enum
+# contents by default.
+globals().update(IDS)
+

spacy/pipeline/_parser_internals/_state.pxd

@@ -17,7 +17,7 @@ from ...typedefs cimport attr_t
 from ...vocab cimport EMPTY_LEXEME
 
 
-cdef inline bint is_space_token(const TokenC* token) nogil:
+cdef inline bint is_space_token(const TokenC* token) noexcept nogil:
     return Lexeme.c_check_flag(token.lex, IS_SPACE)
 
 cdef struct ArcC:
@@ -41,7 +41,7 @@ cdef cppclass StateC:
     int offset
     int _b_i
 
-    __init__(const TokenC* sent, int length) nogil:
+    inline __init__(const TokenC* sent, int length) noexcept nogil:
         this._sent = sent
         this._heads = <int*>calloc(length, sizeof(int))
         if not (this._sent and this._heads):
@@ -57,10 +57,10 @@ cdef cppclass StateC:
         memset(&this._empty_token, 0, sizeof(TokenC))
         this._empty_token.lex = &EMPTY_LEXEME
 
-    __dealloc__():
+    inline __dealloc__():
         free(this._heads)
 
-    void set_context_tokens(int* ids, int n) nogil:
+    inline void set_context_tokens(int* ids, int n) noexcept nogil:
         cdef int i, j
         if n == 1:
             if this.B(0) >= 0:
@@ -131,14 +131,14 @@ cdef cppclass StateC:
             else:
                 ids[i] = -1
 
-    int S(int i) nogil const:
+    inline int S(int i) noexcept nogil const:
         if i >= this._stack.size():
             return -1
         elif i < 0:
             return -1
         return this._stack.at(this._stack.size() - (i+1))
 
-    int B(int i) nogil const:
+    inline int B(int i) noexcept nogil const:
         if i < 0:
             return -1
         elif i < this._rebuffer.size():
@@ -150,19 +150,19 @@ cdef cppclass StateC:
             else:
                 return b_i
 
-    const TokenC* B_(int i) nogil const:
+    inline const TokenC* B_(int i) noexcept nogil const:
         return this.safe_get(this.B(i))
 
-    const TokenC* E_(int i) nogil const:
+    inline const TokenC* E_(int i) noexcept nogil const:
         return this.safe_get(this.E(i))
 
-    const TokenC* safe_get(int i) nogil const:
+    inline const TokenC* safe_get(int i) noexcept nogil const:
         if i < 0 or i >= this.length:
             return &this._empty_token
         else:
             return &this._sent[i]
 
-    void map_get_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, vector[ArcC]* out) nogil const:
+    inline void map_get_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, vector[ArcC]* out) noexcept nogil const:
         cdef const vector[ArcC]* arcs
         head_arcs_it = heads_arcs.const_begin()
         while head_arcs_it != heads_arcs.const_end():
@@ -175,23 +175,23 @@ cdef cppclass StateC:
                 incr(arcs_it)
             incr(head_arcs_it)
 
-    void get_arcs(vector[ArcC]* out) nogil const:
+    inline void get_arcs(vector[ArcC]* out) noexcept nogil const:
         this.map_get_arcs(this._left_arcs, out)
         this.map_get_arcs(this._right_arcs, out)
 
-    int H(int child) nogil const:
+    inline int H(int child) noexcept nogil const:
         if child >= this.length or child < 0:
             return -1
         else:
             return this._heads[child]
 
-    int E(int i) nogil const:
+    inline int E(int i) noexcept nogil const:
         if this._ents.size() == 0:
             return -1
         else:
             return this._ents.back().start
 
-    int nth_child(const unordered_map[int, vector[ArcC]]& heads_arcs, int head, int idx) nogil const:
+    inline int nth_child(const unordered_map[int, vector[ArcC]]& heads_arcs, int head, int idx) noexcept nogil const:
         if idx < 1:
             return -1
 
@@ -215,22 +215,22 @@ cdef cppclass StateC:
 
         return -1
 
-    int L(int head, int idx) nogil const:
+    inline int L(int head, int idx) noexcept nogil const:
         return this.nth_child(this._left_arcs, head, idx)
 
-    int R(int head, int idx) nogil const:
+    inline int R(int head, int idx) noexcept nogil const:
         return this.nth_child(this._right_arcs, head, idx)
 
-    bint empty() nogil const:
+    inline bint empty() noexcept nogil const:
         return this._stack.size() == 0
 
-    bint eol() nogil const:
+    inline bint eol() noexcept nogil const:
         return this.buffer_length() == 0
 
-    bint is_final() nogil const:
+    inline bint is_final() noexcept nogil const:
         return this.stack_depth() <= 0 and this.eol()
 
-    int cannot_sent_start(int word) nogil const:
+    inline int cannot_sent_start(int word) noexcept nogil const:
         if word < 0 or word >= this.length:
             return 0
         elif this._sent[word].sent_start == -1:
@@ -238,7 +238,7 @@ cdef cppclass StateC:
         else:
             return 0
 
-    int is_sent_start(int word) nogil const:
+    inline int is_sent_start(int word) noexcept nogil const:
         if word < 0 or word >= this.length:
             return 0
         elif this._sent[word].sent_start == 1:
@@ -248,20 +248,20 @@ cdef cppclass StateC:
         else:
             return 0
 
-    void set_sent_start(int word, int value) nogil:
+    inline void set_sent_start(int word, int value) noexcept nogil:
         if value >= 1:
             this._sent_starts.insert(word)
 
-    bint has_head(int child) nogil const:
+    inline bint has_head(int child) noexcept nogil const:
         return this._heads[child] >= 0
 
-    int l_edge(int word) nogil const:
+    inline int l_edge(int word) noexcept nogil const:
         return word
 
-    int r_edge(int word) nogil const:
+    inline int r_edge(int word) noexcept nogil const:
         return word
 
-    int n_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, int head) nogil const:
+    inline int n_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, int head) noexcept nogil const:
         cdef int n = 0
         head_arcs_it = heads_arcs.const_find(head)
         if head_arcs_it == heads_arcs.const_end():
@@ -277,28 +277,28 @@ cdef cppclass StateC:
 
         return n
 
-    int n_L(int head) nogil const:
+    inline int n_L(int head) noexcept nogil const:
         return n_arcs(this._left_arcs, head)
 
-    int n_R(int head) nogil const:
+    inline int n_R(int head) noexcept nogil const:
         return n_arcs(this._right_arcs, head)
 
-    bint stack_is_connected() nogil const:
+    inline bint stack_is_connected() noexcept nogil const:
         return False
 
-    bint entity_is_open() nogil const:
+    inline bint entity_is_open() noexcept nogil const:
         if this._ents.size() == 0:
             return False
         else:
             return this._ents.back().end == -1
 
-    int stack_depth() nogil const:
+    inline int stack_depth() noexcept nogil const:
         return this._stack.size()
 
-    int buffer_length() nogil const:
+    inline int buffer_length() noexcept nogil const:
         return (this.length - this._b_i) + this._rebuffer.size()
 
-    void push() nogil:
+    inline void push() noexcept nogil:
         b0 = this.B(0)
         if this._rebuffer.size():
             b0 = this._rebuffer.back()
@@ -308,32 +308,32 @@ cdef cppclass StateC:
             this._b_i += 1
         this._stack.push_back(b0)
 
-    void pop() nogil:
+    inline void pop() noexcept nogil:
         this._stack.pop_back()
 
-    void force_final() nogil:
+    inline void force_final() noexcept nogil:
         # This should only be used in desperate situations, as it may leave
         # the analysis in an unexpected state.
         this._stack.clear()
         this._b_i = this.length
 
-    void unshift() nogil:
+    inline void unshift() noexcept nogil:
         s0 = this._stack.back()
         this._unshiftable[s0] = 1
         this._rebuffer.push_back(s0)
         this._stack.pop_back()
 
-    int is_unshiftable(int item) nogil const:
+    inline int is_unshiftable(int item) noexcept nogil const:
         if item >= this._unshiftable.size():
             return 0
         else:
             return this._unshiftable.at(item)
 
-    void set_reshiftable(int item) nogil:
+    inline void set_reshiftable(int item) noexcept nogil:
         if item < this._unshiftable.size():
             this._unshiftable[item] = 0
 
-    void add_arc(int head, int child, attr_t label) nogil:
+    inline void add_arc(int head, int child, attr_t label) noexcept nogil:
         if this.has_head(child):
             this.del_arc(this.H(child), child)
         cdef ArcC arc
@@ -346,7 +346,7 @@ cdef cppclass StateC:
             this._right_arcs[arc.head].push_back(arc)
         this._heads[child] = head
 
-    void map_del_arc(unordered_map[int, vector[ArcC]]* heads_arcs, int h_i, int c_i) nogil:
+    inline void map_del_arc(unordered_map[int, vector[ArcC]]* heads_arcs, int h_i, int c_i) noexcept nogil:
         arcs_it = heads_arcs.find(h_i)
         if arcs_it == heads_arcs.end():
             return
@@ -367,13 +367,13 @@ cdef cppclass StateC:
                     arc.label = 0
                     break
 
-    void del_arc(int h_i, int c_i) nogil:
+    inline void del_arc(int h_i, int c_i) noexcept nogil:
         if h_i > c_i:
             this.map_del_arc(&this._left_arcs, h_i, c_i)
         else:
             this.map_del_arc(&this._right_arcs, h_i, c_i)
 
-    SpanC get_ent() nogil const:
+    inline SpanC get_ent() noexcept nogil const:
         cdef SpanC ent
         if this._ents.size() == 0:
             ent.start = 0
@@ -383,17 +383,17 @@ cdef cppclass StateC:
         else:
             return this._ents.back()
 
-    void open_ent(attr_t label) nogil:
+    inline void open_ent(attr_t label) noexcept nogil:
         cdef SpanC ent
         ent.start = this.B(0)
         ent.label = label
         ent.end = -1
         this._ents.push_back(ent)
 
-    void close_ent() nogil:
+    inline void close_ent() noexcept nogil:
         this._ents.back().end = this.B(0)+1
 
-    void clone(const StateC* src) nogil:
+    inline void clone(const StateC* src) noexcept nogil:
         this.length = src.length
         this._sent = src._sent
         this._stack = src._stack

spacy/pipeline/_parser_internals/arc_eager.pyx

@@ -155,7 +155,7 @@ cdef GoldParseStateC create_gold_state(
     return gs
 
 
-cdef void update_gold_state(GoldParseStateC* gs, const StateC* s) nogil:
+cdef void update_gold_state(GoldParseStateC* gs, const StateC* s) noexcept nogil:
     for i in range(gs.length):
         gs.state_bits[i] = set_state_flag(
             gs.state_bits[i],
@@ -203,7 +203,7 @@ cdef class ArcEagerGold:
     def __init__(self, ArcEager moves, StateClass stcls, Example example):
         self.mem = Pool()
         heads, labels = example.get_aligned_parse(projectivize=True)
-        labels = [example.x.vocab.strings.add(label) if label is not None else MISSING_DEP for label in labels]
+        labels = [example.x.vocab.strings.add(label, allow_transient=False) if label is not None else MISSING_DEP for label in labels]
         sent_starts = _get_aligned_sent_starts(example)
         assert len(heads) == len(labels) == len(sent_starts), (len(heads), len(labels), len(sent_starts))
         self.c = create_gold_state(self.mem, stcls.c, heads, labels, sent_starts)
@@ -239,12 +239,12 @@ def _get_aligned_sent_starts(example):
         return [None] * len(example.x)
 
 
-cdef int check_state_gold(char state_bits, char flag) nogil:
+cdef int check_state_gold(char state_bits, char flag) noexcept nogil:
     cdef char one = 1
     return 1 if (state_bits & (one << flag)) else 0
 
 
-cdef int set_state_flag(char state_bits, char flag, int value) nogil:
+cdef int set_state_flag(char state_bits, char flag, int value) noexcept nogil:
     cdef char one = 1
     if value:
         return state_bits | (one << flag)
@@ -252,27 +252,27 @@ cdef int set_state_flag(char state_bits, char flag, int value) nogil:
         return state_bits & ~(one << flag)
 
 
-cdef int is_head_in_stack(const GoldParseStateC* gold, int i) nogil:
+cdef int is_head_in_stack(const GoldParseStateC* gold, int i) noexcept nogil:
     return check_state_gold(gold.state_bits[i], HEAD_IN_STACK)
 
 
-cdef int is_head_in_buffer(const GoldParseStateC* gold, int i) nogil:
+cdef int is_head_in_buffer(const GoldParseStateC* gold, int i) noexcept nogil:
     return check_state_gold(gold.state_bits[i], HEAD_IN_BUFFER)
 
 
-cdef int is_head_unknown(const GoldParseStateC* gold, int i) nogil:
+cdef int is_head_unknown(const GoldParseStateC* gold, int i) noexcept nogil:
     return check_state_gold(gold.state_bits[i], HEAD_UNKNOWN)
 
-cdef int is_sent_start(const GoldParseStateC* gold, int i) nogil:
+cdef int is_sent_start(const GoldParseStateC* gold, int i) noexcept nogil:
     return check_state_gold(gold.state_bits[i], IS_SENT_START)
 
-cdef int is_sent_start_unknown(const GoldParseStateC* gold, int i) nogil:
+cdef int is_sent_start_unknown(const GoldParseStateC* gold, int i) noexcept nogil:
     return check_state_gold(gold.state_bits[i], SENT_START_UNKNOWN)
 
 
 # Helper functions for the arc-eager oracle
 
-cdef weight_t push_cost(const StateC* state, const GoldParseStateC* gold) nogil:
+cdef weight_t push_cost(const StateC* state, const GoldParseStateC* gold) noexcept nogil:
     cdef weight_t cost = 0
     b0 = state.B(0)
     if b0 < 0:
@@ -285,7 +285,7 @@ cdef weight_t push_cost(const StateC* state, const GoldParseStateC* gold) nogil:
     return cost
 
 
-cdef weight_t pop_cost(const StateC* state, const GoldParseStateC* gold) nogil:
+cdef weight_t pop_cost(const StateC* state, const GoldParseStateC* gold) noexcept nogil:
     cdef weight_t cost = 0
     s0 = state.S(0)
     if s0 < 0:
@@ -296,7 +296,7 @@ cdef weight_t pop_cost(const StateC* state, const GoldParseStateC* gold) nogil:
     return cost
 
 
-cdef bint arc_is_gold(const GoldParseStateC* gold, int head, int child) nogil:
+cdef bint arc_is_gold(const GoldParseStateC* gold, int head, int child) noexcept nogil:
     if is_head_unknown(gold, child):
         return True
     elif gold.heads[child] == head:
@@ -305,7 +305,7 @@ cdef bint arc_is_gold(const GoldParseStateC* gold, int head, int child) nogil:
         return False
 
 
-cdef bint label_is_gold(const GoldParseStateC* gold, int child, attr_t label) nogil:
+cdef bint label_is_gold(const GoldParseStateC* gold, int child, attr_t label) noexcept nogil:
     if is_head_unknown(gold, child):
         return True
     elif label == 0:
@@ -316,7 +316,7 @@ cdef bint label_is_gold(const GoldParseStateC* gold, int child, attr_t label) no
         return False
 
 
-cdef bint _is_gold_root(const GoldParseStateC* gold, int word) nogil:
+cdef bint _is_gold_root(const GoldParseStateC* gold, int word) noexcept nogil:
     return gold.heads[word] == word or is_head_unknown(gold, word)
 
 
@@ -336,7 +336,7 @@ cdef class Shift:
     * Advance buffer
     """
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         if st.stack_depth() == 0:
             return 1
         elif st.buffer_length() < 2:
@@ -349,11 +349,11 @@ cdef class Shift:
             return 1
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.push()
 
     @staticmethod
-    cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
         gold = <const GoldParseStateC*>_gold
         return gold.push_cost
 
@@ -375,7 +375,7 @@ cdef class Reduce:
         cost by those arcs.
     """
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         if st.stack_depth() == 0:
             return False
         elif st.buffer_length() == 0:
@@ -386,14 +386,14 @@ cdef class Reduce:
             return True
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         if st.has_head(st.S(0)) or st.stack_depth() == 1:
             st.pop()
         else:
             st.unshift()
 
     @staticmethod
-    cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
         gold = <const GoldParseStateC*>_gold
         if state.is_sent_start(state.B(0)):
             return 0
@@ -421,7 +421,7 @@ cdef class LeftArc:
         pop_cost - Arc(B[0], S[0], label) + (Arc(S[1], S[0]) if H(S[0]) else Arcs(S, S[0]))
     """
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         if st.stack_depth() == 0:
             return 0
         elif st.buffer_length() == 0:
@@ -434,7 +434,7 @@ cdef class LeftArc:
             return 1
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.add_arc(st.B(0), st.S(0), label)
         # If we change the stack, it's okay to remove the shifted mark, as
         # we can't get in an infinite loop this way.
@@ -442,7 +442,7 @@ cdef class LeftArc:
         st.pop()
 
     @staticmethod
-    cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
+    cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
         gold = <const GoldParseStateC*>_gold
         cdef weight_t cost = gold.pop_cost
         s0 = state.S(0)
@@ -474,7 +474,7 @@ cdef class RightArc:
         push_cost + (not shifted[b0] and Arc(B[1:], B[0])) - Arc(S[0], B[0], label)
     """
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         if st.stack_depth() == 0:
             return 0
         elif st.buffer_length() == 0:
@@ -488,12 +488,12 @@ cdef class RightArc:
             return 1
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.add_arc(st.S(0), st.B(0), label)
         st.push()
 
     @staticmethod
-    cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
+    cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
         gold = <const GoldParseStateC*>_gold
         cost = gold.push_cost
         s0 = state.S(0)
@@ -525,7 +525,7 @@ cdef class Break:
     * Arcs between S and B[1]
     """
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         if st.buffer_length() < 2:
             return False
         elif st.B(1) != st.B(0) + 1:
@@ -538,11 +538,11 @@ cdef class Break:
             return True
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.set_sent_start(st.B(1), 1)
 
     @staticmethod
-    cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
         gold = <const GoldParseStateC*>_gold
         cdef int b0 = state.B(0)
         cdef int cost = 0
@@ -785,7 +785,7 @@ cdef class ArcEager(TransitionSystem):
         else:
             return False
 
-    cdef int set_valid(self, int* output, const StateC* st) nogil:
+    cdef int set_valid(self, int* output, const StateC* st) noexcept nogil:
         cdef int[N_MOVES] is_valid
         is_valid[SHIFT] = Shift.is_valid(st, 0)
         is_valid[REDUCE] = Reduce.is_valid(st, 0)

spacy/pipeline/_parser_internals/ner.pyx

@@ -110,7 +110,7 @@ cdef void update_gold_state(GoldNERStateC* gs, const StateC* state) except *:
 cdef do_func_t[N_MOVES] do_funcs
 
 
-cdef bint _entity_is_sunk(const StateC* state, Transition* golds) nogil:
+cdef bint _entity_is_sunk(const StateC* state, Transition* golds) noexcept nogil:
     if not state.entity_is_open():
         return False
 
@@ -238,7 +238,7 @@ cdef class BiluoPushDown(TransitionSystem):
 
     def add_action(self, int action, label_name, freq=None):
         cdef attr_t label_id
-        if not isinstance(label_name, (int, long)):
+        if not isinstance(label_name, int):
             label_id = self.strings.add(label_name)
         else:
             label_id = label_name
@@ -347,21 +347,21 @@ cdef class BiluoPushDown(TransitionSystem):
 
 cdef class Missing:
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         return False
 
     @staticmethod
-    cdef int transition(StateC* s, attr_t label) nogil:
+    cdef int transition(StateC* s, attr_t label) noexcept nogil:
         pass
 
     @staticmethod
-    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
         return 9000
 
 
 cdef class Begin:
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         cdef int preset_ent_iob = st.B_(0).ent_iob
         cdef attr_t preset_ent_label = st.B_(0).ent_type
         if st.entity_is_open():
@@ -400,13 +400,13 @@ cdef class Begin:
             return True
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.open_ent(label)
         st.push()
         st.pop()
 
     @staticmethod
-    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
         gold = <GoldNERStateC*>_gold
         b0 = s.B(0)
         cdef int cost = 0
@@ -439,7 +439,7 @@ cdef class Begin:
 
 cdef class In:
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         if not st.entity_is_open():
             return False
         if st.buffer_length() < 2:
@@ -475,12 +475,12 @@ cdef class In:
             return True
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.push()
         st.pop()
 
     @staticmethod
-    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
         gold = <GoldNERStateC*>_gold
         cdef int next_act = gold.ner[s.B(1)].move if s.B(1) >= 0 else OUT
         cdef int g_act = gold.ner[s.B(0)].move
@@ -510,7 +510,7 @@ cdef class In:
 
 cdef class Last:
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         cdef int preset_ent_iob = st.B_(0).ent_iob
         cdef attr_t preset_ent_label = st.B_(0).ent_type
         if label == 0:
@@ -535,13 +535,13 @@ cdef class Last:
             return True
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.close_ent()
         st.push()
         st.pop()
 
     @staticmethod
-    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
         gold = <GoldNERStateC*>_gold
         b0 = s.B(0)
         ent_start = s.E(0)
@@ -581,7 +581,7 @@ cdef class Last:
 
 cdef class Unit:
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         cdef int preset_ent_iob = st.B_(0).ent_iob
         cdef attr_t preset_ent_label = st.B_(0).ent_type
         if label == 0:
@@ -609,14 +609,14 @@ cdef class Unit:
             return True
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.open_ent(label)
         st.close_ent()
         st.push()
         st.pop()
 
     @staticmethod
-    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
         gold = <GoldNERStateC*>_gold
         cdef int g_act = gold.ner[s.B(0)].move
         cdef attr_t g_tag = gold.ner[s.B(0)].label
@@ -646,7 +646,7 @@ cdef class Unit:
 
 cdef class Out:
     @staticmethod
-    cdef bint is_valid(const StateC* st, attr_t label) nogil:
+    cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
         cdef int preset_ent_iob = st.B_(0).ent_iob
         if st.entity_is_open():
             return False
@@ -658,12 +658,12 @@ cdef class Out:
             return True
 
     @staticmethod
-    cdef int transition(StateC* st, attr_t label) nogil:
+    cdef int transition(StateC* st, attr_t label) noexcept nogil:
         st.push()
         st.pop()
 
     @staticmethod
-    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
+    cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
         gold = <GoldNERStateC*>_gold
         cdef int g_act = gold.ner[s.B(0)].move
         cdef weight_t cost = 0

spacy/pipeline/_parser_internals/nonproj.pyx

@@ -94,7 +94,7 @@ cdef bool _has_head_as_ancestor(int tokenid, int head, const vector[int]& heads)
     return False
 
 
-cdef string heads_to_string(const vector[int]& heads) nogil:
+cdef string heads_to_string(const vector[int]& heads) noexcept nogil:
     cdef vector[int].const_iterator citer
     cdef string cycle_str
 
@@ -183,7 +183,7 @@ cpdef deprojectivize(Doc doc):
             new_label, head_label = label.split(DELIMITER)
             new_head = _find_new_head(doc[i], head_label)
             doc.c[i].head = new_head.i - i
-            doc.c[i].dep = doc.vocab.strings.add(new_label)
+            doc.c[i].dep = doc.vocab.strings.add(new_label, allow_transient=False)
     set_children_from_heads(doc.c, 0, doc.length)
     return doc
 

spacy/pipeline/_parser_internals/stateclass.pyx

@@ -29,7 +29,7 @@ cdef class StateClass:
         return [self.B(i) for i in range(self.c.buffer_length())]
 
     @property
-    def token_vector_lenth(self):
+    def token_vector_length(self):
         return self.doc.tensor.shape[1]
 
     @property

spacy/pipeline/_parser_internals/transition_system.pxd

@@ -15,22 +15,22 @@ cdef struct Transition:
 
     weight_t score
 
-    bint (*is_valid)(const StateC* state, attr_t label) nogil
+    bint (*is_valid)(const StateC* state, attr_t label) noexcept nogil
-    weight_t (*get_cost)(const StateC* state, const void* gold, attr_t label) nogil
+    weight_t (*get_cost)(const StateC* state, const void* gold, attr_t label) noexcept nogil
-    int (*do)(StateC* state, attr_t label) nogil
+    int (*do)(StateC* state, attr_t label) noexcept nogil
 
 
 ctypedef weight_t (*get_cost_func_t)(
     const StateC* state, const void* gold, attr_tlabel
-) nogil
+) noexcept nogil
 ctypedef weight_t (*move_cost_func_t)(
         const StateC* state, const void* gold
-) nogil
+) noexcept nogil
 ctypedef weight_t (*label_cost_func_t)(
     const StateC* state, const void* gold, attr_t label
-) nogil
+) noexcept nogil
 
-ctypedef int (*do_func_t)(StateC* state, attr_t label) nogil
+ctypedef int (*do_func_t)(StateC* state, attr_t label) noexcept nogil
 
 ctypedef void* (*init_state_t)(Pool mem, int length, void* tokens) except NULL
 
@@ -53,7 +53,7 @@ cdef class TransitionSystem:
 
     cdef Transition init_transition(self, int clas, int move, attr_t label) except *
 
-    cdef int set_valid(self, int* output, const StateC* st) nogil
+    cdef int set_valid(self, int* output, const StateC* st) noexcept nogil
 
     cdef int set_costs(self, int* is_valid, weight_t* costs,
                        const StateC* state, gold) except -1

spacy/pipeline/_parser_internals/transition_system.pyx

@@ -149,7 +149,7 @@ cdef class TransitionSystem:
         action = self.lookup_transition(move_name)
         return action.is_valid(stcls.c, action.label)
 
-    cdef int set_valid(self, int* is_valid, const StateC* st) nogil:
+    cdef int set_valid(self, int* is_valid, const StateC* st) noexcept nogil:
         cdef int i
         for i in range(self.n_moves):
             is_valid[i] = self.c[i].is_valid(st, self.c[i].label)
@@ -191,8 +191,7 @@ cdef class TransitionSystem:
 
     def add_action(self, int action, label_name):
         cdef attr_t label_id
-        if not isinstance(label_name, int) and \
+        if not isinstance(label_name, int):
-           not isinstance(label_name, long):
             label_id = self.strings.add(label_name)
         else:
             label_id = label_name

spacy/pipeline/attributeruler.py

@@ -1,3 +1,5 @@
+import importlib
+import sys
 from pathlib import Path
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
 
@@ -22,19 +24,6 @@ TagMapType = Dict[str, Dict[Union[int, str], Union[int, str]]]
 MorphRulesType = Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
 
 
-@Language.factory(
-    "attribute_ruler",
-    default_config={
-        "validate": False,
-        "scorer": {"@scorers": "spacy.attribute_ruler_scorer.v1"},
-    },
-)
-def make_attribute_ruler(
-    nlp: Language, name: str, validate: bool, scorer: Optional[Callable]
-):
-    return AttributeRuler(nlp.vocab, name, validate=validate, scorer=scorer)
-
-
 def attribute_ruler_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
     def morph_key_getter(token, attr):
         return getattr(token, attr).key
@@ -54,7 +43,6 @@ def attribute_ruler_score(examples: Iterable[Example], **kwargs) -> Dict[str, An
     return results
 
 
-@registry.scorers("spacy.attribute_ruler_scorer.v1")
 def make_attribute_ruler_scorer():
     return attribute_ruler_score
 
@@ -355,3 +343,11 @@ def _split_morph_attrs(attrs: dict) -> Tuple[dict, dict]:
         else:
             morph_attrs[k] = v
     return other_attrs, morph_attrs
+
+
+# Setup backwards compatibility hook for factories
+def __getattr__(name):
+    if name == "make_attribute_ruler":
+        module = importlib.import_module("spacy.pipeline.factories")
+        return module.make_attribute_ruler
+    raise AttributeError(f"module {__name__} has no attribute {name}")

spacy/pipeline/dep_parser.pyx

@@ -1,4 +1,6 @@
 # cython: infer_types=True, binding=True
+import importlib
+import sys
 from collections import defaultdict
 from typing import Callable, Optional
 
@@ -39,188 +41,6 @@ subword_features = true
 DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
 
 
-@Language.factory(
-    "parser",
-    assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
-    default_config={
-        "moves": None,
-        "update_with_oracle_cut_size": 100,
-        "learn_tokens": False,
-        "min_action_freq": 30,
-        "model": DEFAULT_PARSER_MODEL,
-        "scorer": {"@scorers": "spacy.parser_scorer.v1"},
-    },
-    default_score_weights={
-        "dep_uas": 0.5,
-        "dep_las": 0.5,
-        "dep_las_per_type": None,
-        "sents_p": None,
-        "sents_r": None,
-        "sents_f": 0.0,
-    },
-)
-def make_parser(
-    nlp: Language,
-    name: str,
-    model: Model,
-    moves: Optional[TransitionSystem],
-    update_with_oracle_cut_size: int,
-    learn_tokens: bool,
-    min_action_freq: int,
-    scorer: Optional[Callable],
-):
-    """Create a transition-based DependencyParser component. The dependency parser
-    jointly learns sentence segmentation and labelled dependency parsing, and can
-    optionally learn to merge tokens that had been over-segmented by the tokenizer.
-
-    The parser uses a variant of the non-monotonic arc-eager transition-system
-    described by Honnibal and Johnson (2014), with the addition of a "break"
-    transition to perform the sentence segmentation. Nivre's pseudo-projective
-    dependency transformation is used to allow the parser to predict
-    non-projective parses.
-
-    The parser is trained using an imitation learning objective. The parser follows
-    the actions predicted by the current weights, and at each state, determines
-    which actions are compatible with the optimal parse that could be reached
-    from the current state. The weights such that the scores assigned to the
-    set of optimal actions is increased, while scores assigned to other
-    actions are decreased. Note that more than one action may be optimal for
-    a given state.
-
-    model (Model): The model for the transition-based parser. The model needs
-        to have a specific substructure of named components --- see the
-        spacy.ml.tb_framework.TransitionModel for details.
-    moves (Optional[TransitionSystem]): This defines how the parse-state is created,
-        updated and evaluated. If 'moves' is None, a new instance is
-        created with `self.TransitionSystem()`. Defaults to `None`.
-    update_with_oracle_cut_size (int): During training, cut long sequences into
-        shorter segments by creating intermediate states based on the gold-standard
-        history. The model is not very sensitive to this parameter, so you usually
-        won't need to change it. 100 is a good default.
-    learn_tokens (bool): Whether to learn to merge subtokens that are split
-        relative to the gold standard. Experimental.
-    min_action_freq (int): The minimum frequency of labelled actions to retain.
-        Rarer labelled actions have their label backed-off to "dep". While this
-        primarily affects the label accuracy, it can also affect the attachment
-        structure, as the labels are used to represent the pseudo-projectivity
-        transformation.
-    scorer (Optional[Callable]): The scoring method.
-    """
-    return DependencyParser(
-        nlp.vocab,
-        model,
-        name,
-        moves=moves,
-        update_with_oracle_cut_size=update_with_oracle_cut_size,
-        multitasks=[],
-        learn_tokens=learn_tokens,
-        min_action_freq=min_action_freq,
-        beam_width=1,
-        beam_density=0.0,
-        beam_update_prob=0.0,
-        # At some point in the future we can try to implement support for
-        # partial annotations, perhaps only in the beam objective.
-        incorrect_spans_key=None,
-        scorer=scorer,
-    )
-
-
-@Language.factory(
-    "beam_parser",
-    assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
-    default_config={
-        "beam_width": 8,
-        "beam_density": 0.01,
-        "beam_update_prob": 0.5,
-        "moves": None,
-        "update_with_oracle_cut_size": 100,
-        "learn_tokens": False,
-        "min_action_freq": 30,
-        "model": DEFAULT_PARSER_MODEL,
-        "scorer": {"@scorers": "spacy.parser_scorer.v1"},
-    },
-    default_score_weights={
-        "dep_uas": 0.5,
-        "dep_las": 0.5,
-        "dep_las_per_type": None,
-        "sents_p": None,
-        "sents_r": None,
-        "sents_f": 0.0,
-    },
-)
-def make_beam_parser(
-    nlp: Language,
-    name: str,
-    model: Model,
-    moves: Optional[TransitionSystem],
-    update_with_oracle_cut_size: int,
-    learn_tokens: bool,
-    min_action_freq: int,
-    beam_width: int,
-    beam_density: float,
-    beam_update_prob: float,
-    scorer: Optional[Callable],
-):
-    """Create a transition-based DependencyParser component that uses beam-search.
-    The dependency parser jointly learns sentence segmentation and labelled
-    dependency parsing, and can optionally learn to merge tokens that had been
-    over-segmented by the tokenizer.
-
-    The parser uses a variant of the non-monotonic arc-eager transition-system
-    described by Honnibal and Johnson (2014), with the addition of a "break"
-    transition to perform the sentence segmentation. Nivre's pseudo-projective
-    dependency transformation is used to allow the parser to predict
-    non-projective parses.
-
-    The parser is trained using a global objective. That is, it learns to assign
-    probabilities to whole parses.
-
-    model (Model): The model for the transition-based parser. The model needs
-        to have a specific substructure of named components --- see the
-        spacy.ml.tb_framework.TransitionModel for details.
-    moves (Optional[TransitionSystem]): This defines how the parse-state is created,
-        updated and evaluated. If 'moves' is None, a new instance is
-        created with `self.TransitionSystem()`. Defaults to `None`.
-    update_with_oracle_cut_size (int): During training, cut long sequences into
-        shorter segments by creating intermediate states based on the gold-standard
-        history. The model is not very sensitive to this parameter, so you usually
-        won't need to change it. 100 is a good default.
-    beam_width (int): The number of candidate analyses to maintain.
-    beam_density (float): The minimum ratio between the scores of the first and
-        last candidates in the beam. This allows the parser to avoid exploring
-        candidates that are too far behind. This is mostly intended to improve
-        efficiency, but it can also improve accuracy as deeper search is not
-        always better.
-    beam_update_prob (float): The chance of making a beam update, instead of a
-        greedy update. Greedy updates are an approximation for the beam updates,
-        and are faster to compute.
-    learn_tokens (bool): Whether to learn to merge subtokens that are split
-        relative to the gold standard. Experimental.
-    min_action_freq (int): The minimum frequency of labelled actions to retain.
-        Rarer labelled actions have their label backed-off to "dep". While this
-        primarily affects the label accuracy, it can also affect the attachment
-        structure, as the labels are used to represent the pseudo-projectivity
-        transformation.
-    """
-    return DependencyParser(
-        nlp.vocab,
-        model,
-        name,
-        moves=moves,
-        update_with_oracle_cut_size=update_with_oracle_cut_size,
-        beam_width=beam_width,
-        beam_density=beam_density,
-        beam_update_prob=beam_update_prob,
-        multitasks=[],
-        learn_tokens=learn_tokens,
-        min_action_freq=min_action_freq,
-        # At some point in the future we can try to implement support for
-        # partial annotations, perhaps only in the beam objective.
-        incorrect_spans_key=None,
-        scorer=scorer,
-    )
-
-
 def parser_score(examples, **kwargs):
     """Score a batch of examples.
 
@@ -246,7 +66,6 @@ def parser_score(examples, **kwargs):
     return results
 
 
-@registry.scorers("spacy.parser_scorer.v1")
 def make_parser_scorer():
     return parser_score
 
@@ -346,3 +165,14 @@ cdef class DependencyParser(Parser):
         # because we instead have a label frequency cut-off and back off rare
         # labels to 'dep'.
         pass
+
+
+# Setup backwards compatibility hook for factories
+def __getattr__(name):
+    if name == "make_parser":
+        module = importlib.import_module("spacy.pipeline.factories")
+        return module.make_parser
+    elif name == "make_beam_parser":
+        module = importlib.import_module("spacy.pipeline.factories")
+        return module.make_beam_parser
+    raise AttributeError(f"module {__name__} has no attribute {name}")

spacy/pipeline/edit_tree_lemmatizer.py

@@ -1,3 +1,5 @@
+import importlib
+import sys
 from collections import Counter
 from itertools import islice
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, cast
@@ -39,43 +41,6 @@ subword_features = true
 DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["model"]
 
 
-@Language.factory(
-    "trainable_lemmatizer",
-    assigns=["token.lemma"],
-    requires=[],
-    default_config={
-        "model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
-        "backoff": "orth",
-        "min_tree_freq": 3,
-        "overwrite": False,
-        "top_k": 1,
-        "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
-    },
-    default_score_weights={"lemma_acc": 1.0},
-)
-def make_edit_tree_lemmatizer(
-    nlp: Language,
-    name: str,
-    model: Model,
-    backoff: Optional[str],
-    min_tree_freq: int,
-    overwrite: bool,
-    top_k: int,
-    scorer: Optional[Callable],
-):
-    """Construct an EditTreeLemmatizer component."""
-    return EditTreeLemmatizer(
-        nlp.vocab,
-        model,
-        name,
-        backoff=backoff,
-        min_tree_freq=min_tree_freq,
-        overwrite=overwrite,
-        top_k=top_k,
-        scorer=scorer,
-    )
-
-
 class EditTreeLemmatizer(TrainablePipe):
     """
     Lemmatizer that lemmatizes each word using a predicted edit tree.
@@ -421,3 +386,11 @@ class EditTreeLemmatizer(TrainablePipe):
             self.tree2label[tree_id] = len(self.cfg["labels"])
             self.cfg["labels"].append(tree_id)
         return self.tree2label[tree_id]
+
+
+# Setup backwards compatibility hook for factories
+def __getattr__(name):
+    if name == "make_edit_tree_lemmatizer":
+        module = importlib.import_module("spacy.pipeline.factories")
+        return module.make_edit_tree_lemmatizer
+    raise AttributeError(f"module {__name__} has no attribute {name}")