Merge branch 'master' into fix/windows-quoting

.github/azure-steps.yml

@@ -1,31 +1,30 @@
 parameters:
   python_version: ''
-  architecture: ''
+  architecture: 'x64'
-  prefix: ''
+  num_build_jobs: 2
-  gpu: false
-  num_build_jobs: 1
 
 steps:
   - task: UsePythonVersion@0
     inputs:
       versionSpec: ${{ parameters.python_version }}
       architecture: ${{ parameters.architecture }}
+      allowUnstable: true
 
   - bash: |
       echo "##vso[task.setvariable variable=python_version]${{ parameters.python_version }}"
     displayName: 'Set variables'
 
   - script: |
-      ${{ parameters.prefix }} python -m pip install -U pip setuptools
+      python -m pip install -U build pip setuptools
-      ${{ parameters.prefix }} python -m pip install -U -r requirements.txt
+      python -m pip install -U -r requirements.txt
     displayName: "Install dependencies"
 
   - script: |
-      ${{ parameters.prefix }} python setup.py build_ext --inplace -j ${{ parameters.num_build_jobs }}
+      python -m build --sdist
-      ${{ parameters.prefix }} python setup.py sdist --formats=gztar
+    displayName: "Build sdist"
-    displayName: "Compile and build sdist"
 
-  - script: python -m mypy spacy
+  - script: |
+      python -m mypy spacy
     displayName: 'Run mypy'
     condition: ne(variables['python_version'], '3.6')
 
@@ -34,35 +33,24 @@ steps:
       contents: "spacy"
     displayName: "Delete source directory"
 
+  - task: DeleteFiles@1
+    inputs:
+      contents: "*.egg-info"
+    displayName: "Delete egg-info directory"
+
   - script: |
-      ${{ parameters.prefix }} python -m pip freeze --exclude torch --exclude cupy-cuda110 > installed.txt
+      python -m pip freeze > installed.txt
-      ${{ parameters.prefix }} python -m pip uninstall -y -r installed.txt
+      python -m pip uninstall -y -r installed.txt
     displayName: "Uninstall all packages"
 
   - bash: |
-      ${{ parameters.prefix }} SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
+      SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
-      ${{ parameters.prefix }} SPACY_NUM_BUILD_JOBS=2 python -m pip install dist/$SDIST
+      SPACY_NUM_BUILD_JOBS=${{ parameters.num_build_jobs }} python -m pip install dist/$SDIST
     displayName: "Install from sdist"
 
   - script: |
-      ${{ parameters.prefix }} python -m pip install -U -r requirements.txt
+      python -W error -c "import spacy"
-    displayName: "Install test requirements"
+    displayName: "Test import"
-
-  - script: |
-      ${{ parameters.prefix }} python -m pip install -U cupy-cuda110 -f https://github.com/cupy/cupy/releases/v9.0.0
-      ${{ parameters.prefix }} python -m pip install "torch==1.7.1+cu110" -f https://download.pytorch.org/whl/torch_stable.html
-    displayName: "Install GPU requirements"
-    condition: eq(${{ parameters.gpu }}, true)
-
-  - script: |
-      ${{ parameters.prefix }} python -m pytest --pyargs spacy -W error
-    displayName: "Run CPU tests"
-    condition: eq(${{ parameters.gpu }}, false)
-
-  - script: |
-      ${{ parameters.prefix }} python -m pytest --pyargs spacy -W error -p spacy.tests.enable_gpu
-    displayName: "Run GPU tests"
-    condition: eq(${{ parameters.gpu }}, true)
 
   - script: |
       python -m spacy download ca_core_news_sm
@@ -71,6 +59,11 @@ steps:
     displayName: 'Test download CLI'
     condition: eq(variables['python_version'], '3.8')
 
+  - script: |
+      python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
+    displayName: 'Test no warnings on load (#11713)'
+    condition: eq(variables['python_version'], '3.8')
+
   - script: |
       python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
     displayName: 'Test convert CLI'
@@ -105,13 +98,22 @@ steps:
     displayName: 'Test assemble CLI vectors warning'
     condition: eq(variables['python_version'], '3.8')
 
+  - script: |
+      python -m pip install -U -r requirements.txt
+    displayName: "Install test requirements"
+
+  - script: |
+      python -m pytest --pyargs spacy -W error
+    displayName: "Run CPU tests"
+
+  - script: |
+      python -m pip install --pre thinc-apple-ops
+      python -m pytest --pyargs spacy
+    displayName: "Run CPU tests with thinc-apple-ops"
+    condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.11'))
+
   - script: |
       python .github/validate_universe_json.py website/meta/universe.json
     displayName: 'Test website/meta/universe.json'
     condition: eq(variables['python_version'], '3.8')
 
-  - script: |
-      ${{ parameters.prefix }} python -m pip install --pre thinc-apple-ops
-      ${{ parameters.prefix }} python -m pytest --pyargs spacy
-    displayName: "Run CPU tests with thinc-apple-ops"
-    condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.10'))

.github/workflows/autoblack.yml

@@ -12,10 +12,10 @@ jobs:
     if: github.repository_owner == 'explosion'
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
             ref: ${{ github.head_ref }}
-      - uses: actions/setup-python@v2
+      - uses: actions/setup-python@v4
       - run: pip install black
       - name: Auto-format code if needed
         run: black spacy
@@ -23,10 +23,11 @@ jobs:
       # code and makes GitHub think the action failed
       - name: Check for modified files
         id: git-check
-        run: echo ::set-output name=modified::$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi)
+        run: echo modified=$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi) >> $GITHUB_OUTPUT
+
       - name: Create Pull Request
         if: steps.git-check.outputs.modified == 'true'
-        uses: peter-evans/create-pull-request@v3
+        uses: peter-evans/create-pull-request@v4
         with:
             title: Auto-format code with black
             labels: meta

.github/workflows/explosionbot.yml

@@ -8,14 +8,14 @@ on:
 
 jobs:
   explosion-bot:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-latest
     steps:
       - name: Dump GitHub context
         env:
           GITHUB_CONTEXT: ${{ toJson(github) }}
         run: echo "$GITHUB_CONTEXT"
-      - uses: actions/checkout@v1
+      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v1
+      - uses: actions/setup-python@v4
       - name: Install and run explosion-bot
         run: |
           pip install git+https://${{ secrets.EXPLOSIONBOT_TOKEN }}@github.com/explosion/explosion-bot

.github/workflows/slowtests.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v1
+        uses: actions/checkout@v3
         with:
           ref: ${{ matrix.branch }}
       - name: Get commits from past 24 hours
@@ -23,9 +23,9 @@ jobs:
           today=$(date '+%Y-%m-%d %H:%M:%S')
           yesterday=$(date -d "yesterday" '+%Y-%m-%d %H:%M:%S')
           if git log --after="$yesterday" --before="$today" | grep commit ; then
-            echo "::set-output name=run_tests::true"
+            echo run_tests=true >> $GITHUB_OUTPUT
           else
-            echo "::set-output name=run_tests::false"
+            echo run_tests=false >> $GITHUB_OUTPUT
           fi
 
       - name: Trigger buildkite build

.github/workflows/spacy_universe_alert.yml

@@ -17,8 +17,10 @@ jobs:
         run: |
           echo "$GITHUB_CONTEXT"
 
-      - uses: actions/checkout@v1
+      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v1
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
       - name: Install Bernadette app dependency and send an alert
         env:
           SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}

README.md

@@ -8,7 +8,7 @@ be used in real products.
 
 spaCy comes with
 [pretrained pipelines](https://spacy.io/models) and
-currently supports tokenization and training for **60+ languages**. It features
+currently supports tokenization and training for **70+ languages**. It features
 state-of-the-art speed and **neural network models** for tagging,
 parsing, **named entity recognition**, **text classification** and more,
 multi-task learning with pretrained **transformers** like BERT, as well as a
@@ -16,7 +16,7 @@ production-ready [**training system**](https://spacy.io/usage/training) and easy
 model packaging, deployment and workflow management. spaCy is commercial
 open-source software, released under the MIT license.
 
-💫 **Version 3.4.0 out now!**
+💫 **Version 3.4 out now!**
 [Check out the release notes here.](https://github.com/explosion/spaCy/releases)
 
 [![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
@@ -79,7 +79,7 @@ more people can benefit from it.
 
 ## Features
 
-- Support for **60+ languages**
+- Support for **70+ languages**
 - **Trained pipelines** for different languages and tasks
 - Multi-task learning with pretrained **transformers** like BERT
 - Support for pretrained **word vectors** and embeddings

azure-pipelines.yml

@@ -76,15 +76,24 @@ jobs:
         #        Python39Mac:
         #          imageName: "macos-latest"
         #          python.version: "3.9"
-        Python310Linux:
+        #        Python310Linux:
-          imageName: "ubuntu-latest"
+        #          imageName: "ubuntu-latest"
-          python.version: "3.10"
+        #          python.version: "3.10"
         Python310Windows:
           imageName: "windows-latest"
           python.version: "3.10"
-        Python310Mac:
+        #        Python310Mac:
-          imageName: "macos-latest"
+        #          imageName: "macos-latest"
-          python.version: "3.10"
+        #          python.version: "3.10"
+        Python311Linux:
+          imageName: 'ubuntu-latest'
+          python.version: '3.11'
+        Python311Windows:
+          imageName: 'windows-latest'
+          python.version: '3.11'
+        Python311Mac:
+          imageName: 'macos-latest'
+          python.version: '3.11'
       maxParallel: 4
     pool:
       vmImage: $(imageName)
@@ -92,20 +101,3 @@ jobs:
       - template: .github/azure-steps.yml
         parameters:
           python_version: '$(python.version)'
-          architecture: 'x64'
-
-#  - job: "TestGPU"
-#    dependsOn: "Validate"
-#    strategy:
-#      matrix:
-#        Python38LinuxX64_GPU:
-#          python.version: '3.8'
-#    pool:
-#      name: "LinuxX64_GPU"
-#    steps:
-#      - template: .github/azure-steps.yml
-#        parameters:
-#          python_version: '$(python.version)'
-#          architecture: 'x64'
-#          gpu: true
-#          num_build_jobs: 24

requirements.txt

@@ -9,7 +9,7 @@ murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.1.0
 srsly>=2.4.3,<3.0.0
 catalogue>=2.0.6,<2.1.0
-typer>=0.3.0,<0.5.0
+typer>=0.3.0,<0.8.0
 pathy>=0.3.5
 # Third party dependencies
 numpy>=1.15.0

setup.cfg

@@ -51,7 +51,7 @@ install_requires =
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0
     # Third-party dependencies
-    typer>=0.3.0,<0.5.0
+    typer>=0.3.0,<0.8.0
     pathy>=0.3.5
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0

setup.py

@@ -30,7 +30,9 @@ MOD_NAMES = [
     "spacy.lexeme",
     "spacy.vocab",
     "spacy.attrs",
-    "spacy.kb",
+    "spacy.kb.candidate",
+    "spacy.kb.kb",
+    "spacy.kb.kb_in_memory",
     "spacy.ml.parser_model",
     "spacy.morphology",
     "spacy.pipeline.dep_parser",

spacy/about.py

@@ -1,6 +1,6 @@
 # fmt: off
 __title__ = "spacy"
-__version__ = "3.4.1"
+__version__ = "3.4.2"
 __download_url__ = "https://github.com/explosion/spacy-models/releases/download"
 __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
 __projects__ = "https://github.com/explosion/projects"

spacy/cli/project/remote_storage.py

@@ -10,6 +10,7 @@ from .._util import get_hash, get_checksum, download_file, ensure_pathy
 from ...util import make_tempdir, get_minor_version, ENV_VARS, check_bool_env_var
 from ...git_info import GIT_VERSION
 from ... import about
+from ...errors import Errors
 
 if TYPE_CHECKING:
     from pathy import Pathy  # noqa: F401
@@ -84,7 +85,23 @@ class RemoteStorage:
                 with tarfile.open(tar_loc, mode=mode_string) as tar_file:
                     # This requires that the path is added correctly, relative
                     # to root. This is how we set things up in push()
-                    tar_file.extractall(self.root)
+
+                    # Disallow paths outside the current directory for the tar
+                    # file (CVE-2007-4559, directory traversal vulnerability)
+                    def is_within_directory(directory, target):
+                        abs_directory = os.path.abspath(directory)
+                        abs_target = os.path.abspath(target)
+                        prefix = os.path.commonprefix([abs_directory, abs_target])
+                        return prefix == abs_directory
+
+                    def safe_extract(tar, path):
+                        for member in tar.getmembers():
+                            member_path = os.path.join(path, member.name)
+                            if not is_within_directory(path, member_path):
+                                raise ValueError(Errors.E852)
+                        tar.extractall(path)
+
+                    safe_extract(tar_file, self.root)
         return url
 
     def find(

spacy/cli/project/run.py

@@ -55,6 +55,7 @@ def project_run(
     force: bool = False,
     dry: bool = False,
     capture: bool = False,
+    skip_requirements_check: bool = False,
 ) -> None:
     """Run a named script defined in the project.yml. If the script is a
     command rather than a workflow, it is checked against the project lock file
@@ -70,6 +71,7 @@ def project_run(
         sys.exit will be called with the return code. You should use capture=False
         when you want to turn over execution to the command, and capture=True
         when you want to run the command more like a function.
+    skip_requirements_check (bool): Whether to skip the requirements check.
     """
     config = load_project_config(project_dir, overrides=overrides)
     commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
@@ -77,9 +79,10 @@ def project_run(
     validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)
 
     req_path = project_dir / "requirements.txt"
+    if not skip_requirements_check:
         if config.get("check_requirements", True) and os.path.exists(req_path):
             with req_path.open() as requirements_file:
-            _check_requirements([req.replace("\n", "") for req in requirements_file])
+                _check_requirements([req.strip() for req in requirements_file])
 
     if subcommand in workflows:
         msg.info(f"Running workflow '{subcommand}'")
@@ -91,6 +94,7 @@ def project_run(
                 force=force,
                 dry=dry,
                 capture=capture,
+                skip_requirements_check=True,
             )
     else:
         cmd = commands[subcommand]
@@ -364,6 +368,12 @@ def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]:
             failed_pkgs_msgs.append(dnf.report())
         except pkg_resources.VersionConflict as vc:
             conflicting_pkgs_msgs.append(vc.report())
+        except Exception:
+            msg.warn(
+                f"Unable to check requirement: {req} "
+                "Checks are currently limited to requirement specifiers "
+                "(PEP 508)"
+            )
 
     if len(failed_pkgs_msgs) or len(conflicting_pkgs_msgs):
         msg.warn(

spacy/errors.py

@@ -212,8 +212,8 @@ class Warnings(metaclass=ErrorsWithCodes):
     W121 = ("Attempting to trace non-existent method '{method}' in pipe '{pipe}'")
     W122 = ("Couldn't trace method '{method}' in pipe '{pipe}'. This can happen if the pipe class "
             "is a Cython extension type.")
-    W123 = ("Argument {arg} with value {arg_value} is used instead of {config_value} as specified in the config. Be "
+    W123 = ("Argument `enable` with value {enable} does not contain all values specified in the config option "
-            "aware that this might affect other components in your pipeline.")
+            "`enabled` ({enabled}). Be aware that this might affect other components in your pipeline.")
 
 
 class Errors(metaclass=ErrorsWithCodes):
@@ -540,8 +540,12 @@ class Errors(metaclass=ErrorsWithCodes):
     E199 = ("Unable to merge 0-length span at `doc[{start}:{end}]`.")
     E200 = ("Can't set {attr} from Span.")
     E202 = ("Unsupported {name} mode '{mode}'. Supported modes: {modes}.")
+    E203 = ("If the {name} embedding layer is not updated "
+            "during training, make sure to include it in 'annotating components'")
 
     # New errors added in v3.x
+    E852 = ("The tar file pulled from the remote attempted an unsafe path "
+            "traversal.")
     E853 = ("Unsupported component factory name '{name}'. The character '.' is "
             "not permitted in factory names.")
     E854 = ("Unable to set doc.ents. Check that the 'ents_filter' does not "
@@ -711,9 +715,9 @@ class Errors(metaclass=ErrorsWithCodes):
             "`nlp.enable_pipe` instead.")
     E927 = ("Can't write to frozen list. Maybe you're trying to modify a computed "
             "property or default function argument?")
-    E928 = ("A KnowledgeBase can only be serialized to/from from a directory, "
+    E928 = ("An InMemoryLookupKB can only be serialized to/from from a directory, "
             "but the provided argument {loc} points to a file.")
-    E929 = ("Couldn't read KnowledgeBase from {loc}. The path does not seem to exist.")
+    E929 = ("Couldn't read InMemoryLookupKB from {loc}. The path does not seem to exist.")
     E930 = ("Received invalid get_examples callback in `{method}`. "
             "Expected function that returns an iterable of Example objects but "
             "got: {obj}")
@@ -944,6 +948,12 @@ class Errors(metaclass=ErrorsWithCodes):
              "case pass an empty list for the previously not specified argument to avoid this error.")
     E1043 = ("Expected None or a value in range [{range_start}, {range_end}] for entity linker threshold, but got "
              "{value}.")
+    E1044 = ("Expected `candidates_batch_size` to be >= 1, but got: {value}")
+    E1045 = ("Encountered {parent} subclass without `{parent}.{method}` "
+             "method in '{name}'. If you want to use this method, make "
+             "sure it's overwritten on the subclass.")
+    E1046 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default "
+             "knowledge base, use `InMemoryLookupKB`.")
 
 
 # Deprecated model shortcuts, only used in errors and warnings

spacy/kb/__init__.py

@@ -0,0 +1,3 @@
+from .kb import KnowledgeBase
+from .kb_in_memory import InMemoryLookupKB
+from .candidate import Candidate, get_candidates, get_candidates_batch

spacy/kb/candidate.pxd

@@ -0,0 +1,12 @@
+from .kb cimport KnowledgeBase
+from libcpp.vector cimport vector
+from ..typedefs cimport hash_t
+
+# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
+cdef class Candidate:
+    cdef readonly KnowledgeBase kb
+    cdef hash_t entity_hash
+    cdef float entity_freq
+    cdef vector[float] entity_vector
+    cdef hash_t alias_hash
+    cdef float prior_prob

spacy/kb/candidate.pyx

@@ -0,0 +1,74 @@
+# cython: infer_types=True, profile=True
+
+from typing import Iterable
+from .kb cimport KnowledgeBase
+from ..tokens import Span
+
+cdef class Candidate:
+    """A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
+    to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking
+    algorithm which will disambiguate the various candidates to the correct one.
+    Each candidate (alias, entity) pair is assigned a certain prior probability.
+
+    DOCS: https://spacy.io/api/kb/#candidate-init
+    """
+
+    def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
+        self.kb = kb
+        self.entity_hash = entity_hash
+        self.entity_freq = entity_freq
+        self.entity_vector = entity_vector
+        self.alias_hash = alias_hash
+        self.prior_prob = prior_prob
+
+    @property
+    def entity(self) -> int:
+        """RETURNS (uint64): hash of the entity's KB ID/name"""
+        return self.entity_hash
+
+    @property
+    def entity_(self) -> str:
+        """RETURNS (str): ID/name of this entity in the KB"""
+        return self.kb.vocab.strings[self.entity_hash]
+
+    @property
+    def alias(self) -> int:
+        """RETURNS (uint64): hash of the alias"""
+        return self.alias_hash
+
+    @property
+    def alias_(self) -> str:
+        """RETURNS (str): ID of the original alias"""
+        return self.kb.vocab.strings[self.alias_hash]
+
+    @property
+    def entity_freq(self) -> float:
+        return self.entity_freq
+
+    @property
+    def entity_vector(self) -> Iterable[float]:
+        return self.entity_vector
+
+    @property
+    def prior_prob(self) -> float:
+        return self.prior_prob
+
+
+def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
+    """
+    Return candidate entities for a given mention and fetching appropriate entries from the index.
+    kb (KnowledgeBase): Knowledge base to query.
+    mention (Span): Entity mention for which to identify candidates.
+    RETURNS (Iterable[Candidate]): Identified candidates.
+    """
+    return kb.get_candidates(mention)
+
+
+def get_candidates_batch(kb: KnowledgeBase, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
+    """
+    Return candidate entities for the given mentions and fetching appropriate entries from the index.
+    kb (KnowledgeBase): Knowledge base to query.
+    mention (Iterable[Span]): Entity mentions for which to identify candidates.
+    RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
+    """
+    return kb.get_candidates_batch(mentions)

spacy/kb/kb.pxd

@@ -0,0 +1,10 @@
+"""Knowledge-base for entity or concept linking."""
+
+from cymem.cymem cimport Pool
+from libc.stdint cimport int64_t
+from ..vocab cimport Vocab
+
+cdef class KnowledgeBase:
+    cdef Pool mem
+    cdef readonly Vocab vocab
+    cdef readonly int64_t entity_vector_length

spacy/kb/kb.pyx

@@ -0,0 +1,108 @@
+# cython: infer_types=True, profile=True
+
+from pathlib import Path
+from typing import Iterable, Tuple, Union
+from cymem.cymem cimport Pool
+
+from .candidate import Candidate
+from ..tokens import Span
+from ..util import SimpleFrozenList
+from ..errors import Errors
+
+
+cdef class KnowledgeBase:
+    """A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
+    to support entity linking of named entities to real-world concepts.
+    This is an abstract class and requires its operations to be implemented.
+
+    DOCS: https://spacy.io/api/kb
+    """
+
+    def __init__(self, vocab: Vocab, entity_vector_length: int):
+        """Create a KnowledgeBase."""
+        # Make sure abstract KB is not instantiated.
+        if self.__class__ == KnowledgeBase:
+            raise TypeError(
+                Errors.E1046.format(cls_name=self.__class__.__name__)
+            )
+
+        self.vocab = vocab
+        self.entity_vector_length = entity_vector_length
+        self.mem = Pool()
+
+    def get_candidates_batch(self, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
+        """
+        Return candidate entities for specified texts. Each candidate defines the entity, the original alias,
+        and the prior probability of that alias resolving to that entity.
+        If no candidate is found for a given text, an empty list is returned.
+        mentions (Iterable[Span]): Mentions for which to get candidates.
+        RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
+        """
+        return [self.get_candidates(span) for span in mentions]
+
+    def get_candidates(self, mention: Span) -> Iterable[Candidate]:
+        """
+        Return candidate entities for specified text. Each candidate defines the entity, the original alias,
+        and the prior probability of that alias resolving to that entity.
+        If the no candidate is found for a given text, an empty list is returned.
+        mention (Span): Mention for which to get candidates.
+        RETURNS (Iterable[Candidate]): Identified candidates.
+        """
+        raise NotImplementedError(
+            Errors.E1045.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__)
+        )
+
+    def get_vectors(self, entities: Iterable[str]) -> Iterable[Iterable[float]]:
+        """
+        Return vectors for entities.
+        entity (str): Entity name/ID.
+        RETURNS (Iterable[Iterable[float]]): Vectors for specified entities.
+        """
+        return [self.get_vector(entity) for entity in entities]
+
+    def get_vector(self, str entity) -> Iterable[float]:
+        """
+        Return vector for entity.
+        entity (str): Entity name/ID.
+        RETURNS (Iterable[float]): Vector for specified entity.
+        """
+        raise NotImplementedError(
+            Errors.E1045.format(parent="KnowledgeBase", method="get_vector", name=self.__name__)
+        )
+
+    def to_bytes(self, **kwargs) -> bytes:
+        """Serialize the current state to a binary string.
+        RETURNS (bytes): Current state as binary string.
+        """
+        raise NotImplementedError(
+            Errors.E1045.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__)
+        )
+
+    def from_bytes(self, bytes_data: bytes, *, exclude: Tuple[str] = tuple()):
+        """Load state from a binary string.
+        bytes_data (bytes): KB state.
+        exclude (Tuple[str]): Properties to exclude when restoring KB.
+        """
+        raise NotImplementedError(
+            Errors.E1045.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__)
+        )
+
+    def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
+        """
+        Write KnowledgeBase content to disk.
+        path (Union[str, Path]): Target file path.
+        exclude (Iterable[str]): List of components to exclude.
+        """
+        raise NotImplementedError(
+            Errors.E1045.format(parent="KnowledgeBase", method="to_disk", name=self.__name__)
+        )
+
+    def from_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
+        """
+        Load KnowledgeBase content from disk.
+        path (Union[str, Path]): Target file path.
+        exclude (Iterable[str]): List of components to exclude.
+        """
+        raise NotImplementedError(
+            Errors.E1045.format(parent="KnowledgeBase", method="from_disk", name=self.__name__)
+        )

spacy/kb/kb_in_memory.pxd

@@ -1,14 +1,12 @@
 """Knowledge-base for entity or concept linking."""
-from cymem.cymem cimport Pool
 from preshed.maps cimport PreshMap
 from libcpp.vector cimport vector
 from libc.stdint cimport int32_t, int64_t
 from libc.stdio cimport FILE
 
-from .vocab cimport Vocab
+from ..typedefs cimport hash_t
-from .typedefs cimport hash_t
+from ..structs cimport KBEntryC, AliasC
-from .structs cimport KBEntryC, AliasC
+from .kb cimport KnowledgeBase
-
 
 ctypedef vector[KBEntryC] entry_vec
 ctypedef vector[AliasC] alias_vec
@@ -16,21 +14,7 @@ ctypedef vector[float] float_vec
 ctypedef vector[float_vec] float_matrix
 
 
-# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
+cdef class InMemoryLookupKB(KnowledgeBase):
-cdef class Candidate:
-    cdef readonly KnowledgeBase kb
-    cdef hash_t entity_hash
-    cdef float entity_freq
-    cdef vector[float] entity_vector
-    cdef hash_t alias_hash
-    cdef float prior_prob
-
-
-cdef class KnowledgeBase:
-    cdef Pool mem
-    cdef readonly Vocab vocab
-    cdef int64_t entity_vector_length
-
     # This maps 64bit keys (hash of unique entity string)
     # to 64bit values (position of the _KBEntryC struct in the _entries vector).
     # The PreshMap is pretty space efficient, as it uses open addressing. So

spacy/kb/kb_in_memory.pyx

@@ -1,8 +1,7 @@
 # cython: infer_types=True, profile=True
-from typing import Iterator, Iterable, Callable, Dict, Any
+from typing import Iterable, Callable, Dict, Any, Union
 
 import srsly
-from cymem.cymem cimport Pool
 from preshed.maps cimport PreshMap
 from cpython.exc cimport PyErr_SetFromErrno
 from libc.stdio cimport fopen, fclose, fread, fwrite, feof, fseek
@@ -12,85 +11,28 @@ from libcpp.vector cimport vector
 from pathlib import Path
 import warnings
 
-from .typedefs cimport hash_t
+from ..tokens import Span
-from .errors import Errors, Warnings
+from ..typedefs cimport hash_t
-from . import util
+from ..errors import Errors, Warnings
-from .util import SimpleFrozenList, ensure_path
+from .. import util
-
+from ..util import SimpleFrozenList, ensure_path
-cdef class Candidate:
+from ..vocab cimport Vocab
-    """A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
+from .kb cimport KnowledgeBase
-    to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking
+from .candidate import Candidate as Candidate
-    algorithm which will disambiguate the various candidates to the correct one.
-    Each candidate (alias, entity) pair is assigned to a certain prior probability.
-
-    DOCS: https://spacy.io/api/kb/#candidate_init
-    """
-
-    def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
-        self.kb = kb
-        self.entity_hash = entity_hash
-        self.entity_freq = entity_freq
-        self.entity_vector = entity_vector
-        self.alias_hash = alias_hash
-        self.prior_prob = prior_prob
-
-    @property
-    def entity(self):
-        """RETURNS (uint64): hash of the entity's KB ID/name"""
-        return self.entity_hash
-
-    @property
-    def entity_(self):
-        """RETURNS (str): ID/name of this entity in the KB"""
-        return self.kb.vocab.strings[self.entity_hash]
-
-    @property
-    def alias(self):
-        """RETURNS (uint64): hash of the alias"""
-        return self.alias_hash
-
-    @property
-    def alias_(self):
-        """RETURNS (str): ID of the original alias"""
-        return self.kb.vocab.strings[self.alias_hash]
-
-    @property
-    def entity_freq(self):
-        return self.entity_freq
-
-    @property
-    def entity_vector(self):
-        return self.entity_vector
-
-    @property
-    def prior_prob(self):
-        return self.prior_prob
 
 
-def get_candidates(KnowledgeBase kb, span) -> Iterator[Candidate]:
+cdef class InMemoryLookupKB(KnowledgeBase):
-    """
+    """An `InMemoryLookupKB` instance stores unique identifiers for entities and their textual aliases,
-    Return candidate entities for a given span by using the text of the span as the alias
-    and fetching appropriate entries from the index.
-    This particular function is optimized to work with the built-in KB functionality,
-    but any other custom candidate generation method can be used in combination with the KB as well.
-    """
-    return kb.get_alias_candidates(span.text)
-
-
-cdef class KnowledgeBase:
-    """A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
     to support entity linking of named entities to real-world concepts.
 
-    DOCS: https://spacy.io/api/kb
+    DOCS: https://spacy.io/api/kb_in_memory
     """
 
     def __init__(self, Vocab vocab, entity_vector_length):
-        """Create a KnowledgeBase."""
+        """Create an InMemoryLookupKB."""
-        self.mem = Pool()
+        super().__init__(vocab, entity_vector_length)
-        self.entity_vector_length = entity_vector_length
         self._entry_index = PreshMap()
         self._alias_index = PreshMap()
-        self.vocab = vocab
         self._create_empty_vectors(dummy_hash=self.vocab.strings[""])
 
     def _initialize_entities(self, int64_t nr_entities):
@@ -104,11 +46,6 @@ cdef class KnowledgeBase:
         self._alias_index = PreshMap(nr_aliases + 1)
         self._aliases_table = alias_vec(nr_aliases + 1)
 
-    @property
-    def entity_vector_length(self):
-        """RETURNS (uint64): length of the entity vectors"""
-        return self.entity_vector_length
-
     def __len__(self):
         return self.get_size_entities()
 
@@ -286,7 +223,10 @@ cdef class KnowledgeBase:
             alias_entry.probs = probs
             self._aliases_table[alias_index] = alias_entry
 
-    def get_alias_candidates(self, str alias) -> Iterator[Candidate]:
+    def get_candidates(self, mention: Span) -> Iterable[Candidate]:
+        return self.get_alias_candidates(mention.text)  # type: ignore
+
+    def get_alias_candidates(self, str alias) -> Iterable[Candidate]:
         """
         Return candidate entities for an alias. Each candidate defines the entity, the original alias,
         and the prior probability of that alias resolving to that entity.

spacy/lang/ca/lemmatizer.py

@@ -72,10 +72,10 @@ class CatalanLemmatizer(Lemmatizer):
                         oov_forms.append(form)
         if not forms:
             forms.extend(oov_forms)
-        if not forms and string in lookup_table.keys():
+
-            forms.append(self.lookup_lemmatize(token)[0])
+        # use lookups, and fall back to the token itself
         if not forms:
-            forms.append(string)
+            forms.append(lookup_table.get(string, [string])[0])
         forms = list(dict.fromkeys(forms))
         self.cache[cache_key] = forms
         return forms

spacy/lang/char_classes.py

@@ -280,7 +280,7 @@ _currency = (
 _punct = (
     r"… …… , : ; \! \? ¿ ؟ ¡ \( \) \[ \] \{ \} < > _ # \* & 。 ？ ！ ， 、 ； ： ～ · । ، ۔ ؛ ٪"
 )
-_quotes = r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉'
+_quotes = r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉 〈 〉  ⟦ ⟧'
 _hyphens = "- – — -- --- —— ~"
 
 # Various symbols like dingbats, but also emoji

spacy/lang/fr/lemmatizer.py

@@ -53,11 +53,16 @@ class FrenchLemmatizer(Lemmatizer):
         rules = rules_table.get(univ_pos, [])
         string = string.lower()
         forms = []
+        # first try lookup in table based on upos
         if string in index:
             forms.append(string)
             self.cache[cache_key] = forms
             return forms
+
+        # then add anything in the exceptions table
         forms.extend(exceptions.get(string, []))
+
+        # if nothing found yet, use the rules
         oov_forms = []
         if not forms:
             for old, new in rules:
@@ -69,12 +74,14 @@ class FrenchLemmatizer(Lemmatizer):
                         forms.append(form)
                     else:
                         oov_forms.append(form)
+
+        # if still nothing, add the oov forms from rules
         if not forms:
             forms.extend(oov_forms)
-        if not forms and string in lookup_table.keys():
+
-            forms.append(self.lookup_lemmatize(token)[0])
+        # use lookups, which fall back to the token itself
         if not forms:
-            forms.append(string)
+            forms.append(lookup_table.get(string, [string])[0])
         forms = list(dict.fromkeys(forms))
         self.cache[cache_key] = forms
         return forms

spacy/lang/grc/__init__.py

@@ -1,11 +1,15 @@
 from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
 from .stop_words import STOP_WORDS
 from .lex_attrs import LEX_ATTRS
+from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
 from ...language import Language, BaseDefaults
 
 
 class AncientGreekDefaults(BaseDefaults):
     tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    prefixes = TOKENIZER_PREFIXES
+    suffixes = TOKENIZER_SUFFIXES
+    infixes = TOKENIZER_INFIXES
     lex_attr_getters = LEX_ATTRS
     stop_words = STOP_WORDS
 

spacy/lang/grc/punctuation.py

@@ -0,0 +1,46 @@
+from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, LIST_CURRENCY
+from ..char_classes import LIST_ICONS, ALPHA_LOWER, ALPHA_UPPER, ALPHA, HYPHENS
+from ..char_classes import CONCAT_QUOTES
+
+_prefixes = (
+    [
+        "†",
+        "⸏",
+    ]
+    + LIST_PUNCT
+    + LIST_ELLIPSES
+    + LIST_QUOTES
+    + LIST_CURRENCY
+    + LIST_ICONS
+)
+
+_suffixes = (
+    LIST_PUNCT
+    + LIST_ELLIPSES
+    + LIST_QUOTES
+    + LIST_ICONS
+    + [
+        "†",
+        "⸎",
+        r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])[\-\.⸏]",
+    ]
+)
+
+_infixes = (
+    LIST_ELLIPSES
+    + LIST_ICONS
+    + [
+        r"(?<=[0-9])[+\-\*^](?=[0-9-])",
+        r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
+            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
+        ),
+        r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
+        r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
+        r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])—",
+    ]
+)
+
+TOKENIZER_PREFIXES = _prefixes
+TOKENIZER_SUFFIXES = _suffixes
+TOKENIZER_INFIXES = _infixes

spacy/lang/ru/__init__.py

@@ -28,7 +28,7 @@ class Russian(Language):
     assigns=["token.lemma"],
     default_config={
         "model": None,
-        "mode": "pymorphy2",
+        "mode": "pymorphy3",
         "overwrite": False,
         "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
     },

spacy/lang/ru/lemmatizer.py

@@ -19,7 +19,7 @@ class RussianLemmatizer(Lemmatizer):
         model: Optional[Model],
         name: str = "lemmatizer",
         *,
-        mode: str = "pymorphy2",
+        mode: str = "pymorphy3",
         overwrite: bool = False,
         scorer: Optional[Callable] = lemmatizer_score,
     ) -> None:
@@ -33,6 +33,16 @@ class RussianLemmatizer(Lemmatizer):
                 ) from None
             if getattr(self, "_morph", None) is None:
                 self._morph = MorphAnalyzer()
+        elif mode == "pymorphy3":
+            try:
+                from pymorphy3 import MorphAnalyzer
+            except ImportError:
+                raise ImportError(
+                    "The Russian lemmatizer mode 'pymorphy3' requires the "
+                    "pymorphy3 library. Install it with: pip install pymorphy3"
+                ) from None
+            if getattr(self, "_morph", None) is None:
+                self._morph = MorphAnalyzer()
         super().__init__(
             vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
         )
@@ -104,6 +114,9 @@ class RussianLemmatizer(Lemmatizer):
             return [analyses[0].normal_form]
         return [string]
 
+    def pymorphy3_lemmatize(self, token: Token) -> List[str]:
+        return self.pymorphy2_lemmatize(token)
+
 
 def oc2ud(oc_tag: str) -> Tuple[str, Dict[str, str]]:
     gram_map = {

spacy/lang/sl/__init__.py

@@ -1,9 +1,17 @@
+from .lex_attrs import LEX_ATTRS
+from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES, TOKENIZER_PREFIXES
 from .stop_words import STOP_WORDS
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
 from ...language import Language, BaseDefaults
 
 
 class SlovenianDefaults(BaseDefaults):
     stop_words = STOP_WORDS
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    prefixes = TOKENIZER_PREFIXES
+    infixes = TOKENIZER_INFIXES
+    suffixes = TOKENIZER_SUFFIXES
+    lex_attr_getters = LEX_ATTRS
 
 
 class Slovenian(Language):

spacy/lang/sl/lex_attrs.py

@@ -0,0 +1,145 @@
+from ...attrs import LIKE_NUM
+from ...attrs import IS_CURRENCY
+import unicodedata
+
+
+_num_words = set(
+    """
+	nula ničla nič ena dva tri štiri pet šest sedem osem
+	devet deset enajst dvanajst trinajst štirinajst petnajst
+	šestnajst sedemnajst osemnajst devetnajst dvajset trideset štirideset
+	petdeset šestdest sedemdeset osemdeset devedeset sto tisoč
+	milijon bilijon trilijon kvadrilijon nešteto
+	
+	en eden enega enemu ennem enim enih enima enimi ene eni eno
+	dveh dvema dvem dvoje trije treh trem tremi troje štirje štirih štirim štirimi
+	petih petim petimi šestih šestim šestimi sedmih sedmim sedmimi osmih osmim osmimi
+	devetih devetim devetimi desetih desetim desetimi enajstih enajstim enajstimi
+	dvanajstih dvanajstim dvanajstimi trinajstih trinajstim trinajstimi
+	šestnajstih šestnajstim šestnajstimi petnajstih petnajstim petnajstimi
+	sedemnajstih sedemnajstim sedemnajstimi osemnajstih osemnajstim osemnajstimi
+	devetnajstih devetnajstim devetnajstimi dvajsetih dvajsetim dvajsetimi  
+	""".split()
+)
+
+_ordinal_words = set(
+    """
+	prvi drugi tretji četrti peti šesti sedmi osmi
+	deveti deseti enajsti dvanajsti trinajsti štirinajsti
+	petnajsti šestnajsti sedemnajsti osemnajsti devetnajsti
+	dvajseti trideseti štirideseti petdeseti šestdeseti sedemdeseti
+	osemdeseti devetdeseti stoti tisoči milijonti bilijonti
+	trilijonti kvadrilijonti nešteti
+	
+	prva druga tretja četrta peta šesta sedma osma
+	deveta deseta enajsta dvanajsta trinajsta štirnajsta
+	petnajsta šestnajsta sedemnajsta osemnajsta devetnajsta
+	dvajseta trideseta štirideseta petdeseta šestdeseta sedemdeseta
+	osemdeseta devetdeseta stota tisoča milijonta bilijonta
+	trilijonta kvadrilijonta nešteta
+	
+	prvo drugo tretje četrto peto šestro sedmo osmo
+	deveto deseto enajsto dvanajsto trinajsto štirnajsto
+	petnajsto šestnajsto sedemnajsto osemnajsto devetnajsto
+	dvajseto trideseto štirideseto petdeseto šestdeseto sedemdeseto
+	osemdeseto devetdeseto stoto tisočo milijonto bilijonto
+	trilijonto kvadrilijonto nešteto
+	
+	prvega drugega tretjega četrtega petega šestega sedmega osmega 
+	devega desetega enajstega dvanajstega trinajstega štirnajstega
+	petnajstega šestnajstega sedemnajstega osemnajstega devetnajstega
+	dvajsetega tridesetega štiridesetega petdesetega šestdesetega sedemdesetega
+	osemdesetega devetdesetega stotega tisočega milijontega bilijontega
+	trilijontega kvadrilijontega neštetega
+	
+	prvemu drugemu tretjemu četrtemu petemu šestemu sedmemu osmemu devetemu desetemu 
+	enajstemu dvanajstemu trinajstemu štirnajstemu petnajstemu šestnajstemu sedemnajstemu
+	osemnajstemu devetnajstemu dvajsetemu tridesetemu štiridesetemu petdesetemu šestdesetemu
+	sedemdesetemu osemdesetemu devetdesetemu stotemu tisočemu milijontemu bilijontemu
+	trilijontemu kvadrilijontemu neštetemu
+	
+	prvem drugem tretjem četrtem petem šestem sedmem osmem devetem desetem
+	enajstem dvanajstem trinajstem štirnajstem petnajstem šestnajstem sedemnajstem
+	osemnajstem devetnajstem dvajsetem tridesetem štiridesetem petdesetem šestdesetem
+	sedemdesetem osemdesetem devetdesetem stotem tisočem milijontem bilijontem
+	trilijontem kvadrilijontem neštetem
+	
+	prvim drugim tretjim četrtim petim šestim sedtim osmim devetim desetim
+	enajstim dvanajstim trinajstim štirnajstim petnajstim šestnajstim sedemnajstim
+	osemnajstim devetnajstim dvajsetim tridesetim štiridesetim petdesetim šestdesetim
+	sedemdesetim osemdesetim devetdesetim stotim tisočim milijontim bilijontim
+	trilijontim kvadrilijontim neštetim
+	    
+	prvih drugih tretjih četrthih petih šestih sedmih osmih deveth desetih
+	enajstih dvanajstih trinajstih štirnajstih petnajstih šestnajstih sedemnajstih
+	osemnajstih devetnajstih dvajsetih tridesetih štiridesetih petdesetih šestdesetih
+	sedemdesetih osemdesetih devetdesetih stotih tisočih milijontih bilijontih
+	trilijontih kvadrilijontih nešteth
+	
+	prvima drugima tretjima četrtima petima šestima sedmima osmima devetima desetima
+	enajstima dvanajstima trinajstima štirnajstima petnajstima šestnajstima sedemnajstima
+	osemnajstima devetnajstima dvajsetima tridesetima štiridesetima petdesetima šestdesetima
+	sedemdesetima osemdesetima devetdesetima stotima tisočima milijontima bilijontima
+	trilijontima kvadrilijontima neštetima
+	
+	prve druge četrte pete šeste sedme osme devete desete
+	enajste dvanajste trinajste štirnajste petnajste šestnajste sedemnajste
+	osemnajste devetnajste dvajsete tridesete štiridesete petdesete šestdesete
+	sedemdesete osemdesete devetdesete stote tisoče milijonte bilijonte 
+	trilijonte kvadrilijonte neštete
+	
+	prvimi drugimi tretjimi četrtimi petimi šestimi sedtimi osmimi devetimi desetimi
+	enajstimi dvanajstimi trinajstimi štirnajstimi petnajstimi šestnajstimi sedemnajstimi
+	osemnajstimi devetnajstimi dvajsetimi tridesetimi štiridesetimi petdesetimi šestdesetimi
+	sedemdesetimi osemdesetimi devetdesetimi stotimi tisočimi milijontimi bilijontimi
+	trilijontimi kvadrilijontimi neštetimi
+	""".split()
+)
+
+_currency_words = set(
+    """
+	evro evra evru evrom evrov evroma evrih evrom evre evri evr eur
+	cent centa centu cenom centov centoma centih centom cente centi
+	dolar dolarja dolarji dolarju dolarjem dolarjev dolarjema dolarjih dolarje usd
+	tolar tolarja tolarji tolarju tolarjem tolarjev tolarjema tolarjih tolarje tol
+	dinar dinarja dinarji dinarju dinarjem dinarjev dinarjema dinarjih dinarje din
+	funt funta funti funtu funtom funtov funtoma funtih funte gpb
+	forint forinta forinti forintu forintom forintov forintoma forintih forinte
+	zlot zlota zloti zlotu zlotom zlotov zlotoma zlotih zlote 
+	rupij rupija rupiji rupiju rupijem rupijev rupijema rupijih rupije
+	jen jena jeni jenu jenom jenov jenoma jenih jene
+	kuna kuni kune kuno kun kunama kunah kunam kunami
+	marka marki marke markama markah markami 
+	""".split()
+)
+
+
+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
+    if text_lower in _ordinal_words:
+        return True
+    return False
+
+
+def is_currency(text):
+    text_lower = text.lower()
+    if text in _currency_words:
+        return True
+    for char in text:
+        if unicodedata.category(char) != "Sc":
+            return False
+    return True
+
+
+LEX_ATTRS = {LIKE_NUM: like_num, IS_CURRENCY: is_currency}

spacy/lang/sl/punctuation.py

@@ -0,0 +1,84 @@
+from ..char_classes import (
+    LIST_ELLIPSES,
+    LIST_ICONS,
+    HYPHENS,
+    LIST_PUNCT,
+    LIST_QUOTES,
+    CURRENCY,
+    UNITS,
+    PUNCT,
+    LIST_CURRENCY,
+    CONCAT_QUOTES,
+)
+from ..char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA
+from ..char_classes import merge_chars
+from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
+
+
+INCLUDE_SPECIAL = ["\\+", "\\/", "\\•", "\\¯", "\\=", "\\×"] + HYPHENS.split("|")
+
+_prefixes = INCLUDE_SPECIAL + BASE_TOKENIZER_PREFIXES
+
+_suffixes = (
+    INCLUDE_SPECIAL
+    + LIST_PUNCT
+    + LIST_ELLIPSES
+    + LIST_QUOTES
+    + LIST_ICONS
+    + [
+        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=CONCAT_QUOTES, p=PUNCT
+        ),
+        r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
+        # split initials like J.K. Rowling
+        r"(?<=[A-Z]\.)(?:[A-Z].)",
+    ]
+)
+
+# a list of all suffixes following a hyphen that are shouldn't split (eg. BTC-jev)
+# source: Obeliks tokenizer - https://github.com/clarinsi/obeliks/blob/master/obeliks/res/TokRulesPart1.txt
+CONCAT_QUOTES = CONCAT_QUOTES.replace("'", "")
+HYPHENS_PERMITTED = (
+    "((a)|(evemu)|(evskega)|(i)|(jevega)|(jevska)|(jevskimi)|(jinemu)|(oma)|(ovim)|"
+    "(ovski)|(e)|(evi)|(evskem)|(ih)|(jevem)|(jevske)|(jevsko)|(jini)|(ov)|(ovima)|"
+    "(ovskih)|(em)|(evih)|(evskemu)|(ja)|(jevemu)|(jevskega)|(ji)|(jinih)|(ova)|"
+    "(ovimi)|(ovskim)|(ema)|(evim)|(evski)|(je)|(jevi)|(jevskem)|(jih)|(jinim)|"
+    "(ove)|(ovo)|(ovskima)|(ev)|(evima)|(evskih)|(jem)|(jevih)|(jevskemu)|(jin)|"
+    "(jinima)|(ovega)|(ovska)|(ovskimi)|(eva)|(evimi)|(evskim)|(jema)|(jevim)|"
+    "(jevski)|(jina)|(jinimi)|(ovem)|(ovske)|(ovsko)|(eve)|(evo)|(evskima)|(jev)|"
+    "(jevima)|(jevskih)|(jine)|(jino)|(ovemu)|(ovskega)|(u)|(evega)|(evska)|"
+    "(evskimi)|(jeva)|(jevimi)|(jevskim)|(jinega)|(ju)|(ovi)|(ovskem)|(evem)|"
+    "(evske)|(evsko)|(jeve)|(jevo)|(jevskima)|(jinem)|(om)|(ovih)|(ovskemu)|"
+    "(ovec)|(ovca)|(ovcu)|(ovcem)|(ovcev)|(ovcema)|(ovcih)|(ovci)|(ovce)|(ovcimi)|"
+    "(evec)|(evca)|(evcu)|(evcem)|(evcev)|(evcema)|(evcih)|(evci)|(evce)|(evcimi)|"
+    "(jevec)|(jevca)|(jevcu)|(jevcem)|(jevcev)|(jevcema)|(jevcih)|(jevci)|(jevce)|"
+    "(jevcimi)|(ovka)|(ovke)|(ovki)|(ovko)|(ovk)|(ovkama)|(ovkah)|(ovkam)|(ovkami)|"
+    "(evka)|(evke)|(evki)|(evko)|(evk)|(evkama)|(evkah)|(evkam)|(evkami)|(jevka)|"
+    "(jevke)|(jevki)|(jevko)|(jevk)|(jevkama)|(jevkah)|(jevkam)|(jevkami)|(timi)|"
+    "(im)|(ima)|(a)|(imi)|(e)|(o)|(ega)|(ti)|(em)|(tih)|(emu)|(tim)|(i)|(tima)|"
+    "(ih)|(ta)|(te)|(to)|(tega)|(tem)|(temu))"
+)
+
+_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})(?!{hp}$)(?=[{a}])".format(
+            a=ALPHA, h=HYPHENS, hp=HYPHENS_PERMITTED
+        ),
+        r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
+    ]
+)
+
+
+TOKENIZER_PREFIXES = _prefixes
+TOKENIZER_SUFFIXES = _suffixes
+TOKENIZER_INFIXES = _infixes

spacy/lang/sl/stop_words.py

@@ -1,326 +1,84 @@
 # Source: https://github.com/stopwords-iso/stopwords-sl
-# Removed various words that are not normally considered stop words, such as months.
 
 STOP_WORDS = set(
     """
-a
+a ali 
-ali
+
-b
+b bi bil bila bile bili bilo biti blizu bo bodo bojo bolj bom bomo 
-bi
+boste bova boš brez
-bil
+
-bila
+c cel cela celi celo
-bile
+
-bili
+č če često četrta četrtek četrti četrto čez čigav
-bilo
+
-biti
+d da daleč dan danes datum deset deseta deseti deseto devet
-blizu
+deveta deveti deveto do dober dobra dobri dobro dokler dol dolg
-bo
+dolga dolgi dovolj drug druga drugi drugo dva dve
-bodo
+
-bolj
+e eden en ena ene eni enkrat eno etc.
-bom
+
-bomo
-boste
-bova
-boš
-brez
-c
-cel
-cela
-celi
-celo
-d
-da
-daleč
-dan
-danes
-do
-dober
-dobra
-dobri
-dobro
-dokler
-dol
-dovolj
-e
-eden
-en
-ena
-ene
-eni
-enkrat
-eno
-etc.
 f
-g
+
-g.
+g g. ga ga. gor gospa gospod 
-ga
+
-ga.
+h halo 
-gor
+
-gospa
+i idr. ii iii in iv ix iz
-gospod
+
-h
+j jaz je ji jih jim jo jutri
-halo
+
-i
+k kadarkoli kaj kajti kako kakor kamor kamorkoli kar karkoli
-idr.
+katerikoli kdaj kdo kdorkoli ker ki kje kjer kjerkoli
-ii
+ko koder koderkoli koga komu kot kratek kratka kratke kratki
-iii
+
-in
+l lahka lahke lahki lahko le lep lepa lepe lepi lepo leto
-iv
+
-ix
+m majhen majhna majhni malce malo manj me med medtem mene
-iz
+mesec mi midva midve mnogo moj moja moje mora morajo moram
-j
+moramo morate moraš morem mu
-jaz
+
-je
+n na nad naj najina najino najmanj naju največ nam narobe
-ji
+nas nato nazaj naš naša naše ne nedavno nedelja nek neka
-jih
+nekaj nekatere nekateri nekatero nekdo neke nekega neki
-jim
+nekje neko nekoga nekoč ni nikamor nikdar nikjer nikoli
-jo
+nič nje njega njegov njegova njegovo njej njemu njen
-k
+njena njeno nji njih njihov njihova njihovo njiju njim
-kadarkoli
+njo njun njuna njuno no nocoj npr.
-kaj
+
-kajti
+o ob oba obe oboje od odprt odprta odprti okoli on
-kako
+onadva one oni onidve osem osma osmi osmo oz.
-kakor
+
-kamor
+p pa pet peta petek peti peto po pod pogosto poleg poln
-kamorkoli
+polna polni polno ponavadi ponedeljek ponovno potem
-kar
+povsod pozdravljen pozdravljeni prav prava prave pravi
-karkoli
+pravo prazen prazna prazno prbl. precej pred prej preko
-katerikoli
+pri pribl. približno primer pripravljen pripravljena
-kdaj
+pripravljeni proti prva prvi prvo
-kdo
+
-kdorkoli
+r ravno redko res reč
-ker
+
-ki
+s saj sam sama same sami samo se sebe sebi sedaj sedem
-kje
+sedma sedmi sedmo sem seveda si sicer skoraj skozi slab sm
-kjer
+so sobota spet sreda srednja srednji sta ste stran stvar sva
-kjerkoli
+
-ko
+š šest šesta šesti šesto štiri 
-koderkoli
+
-koga
+t ta tak taka take taki tako takoj tam te tebe tebi tega
-komu
+težak težka težki težko ti tista tiste tisti tisto tj.
-kot
+tja to toda torek tretja tretje tretji tri tu tudi tukaj
-l
+tvoj tvoja tvoje
-le
+
-lep
-lepa
-lepe
-lepi
-lepo
-m
-manj
-me
-med
-medtem
-mene
-mi
-midva
-midve
-mnogo
-moj
-moja
-moje
-mora
-morajo
-moram
-moramo
-morate
-moraš
-morem
-mu
-n
-na
-nad
-naj
-najina
-najino
-najmanj
-naju
-največ
-nam
-nas
-nato
-nazaj
-naš
-naša
-naše
-ne
-nedavno
-nek
-neka
-nekaj
-nekatere
-nekateri
-nekatero
-nekdo
-neke
-nekega
-neki
-nekje
-neko
-nekoga
-nekoč
-ni
-nikamor
-nikdar
-nikjer
-nikoli
-nič
-nje
-njega
-njegov
-njegova
-njegovo
-njej
-njemu
-njen
-njena
-njeno
-nji
-njih
-njihov
-njihova
-njihovo
-njiju
-njim
-njo
-njun
-njuna
-njuno
-no
-nocoj
-npr.
-o
-ob
-oba
-obe
-oboje
-od
-okoli
-on
-onadva
-one
-oni
-onidve
-oz.
-p
-pa
-po
-pod
-pogosto
-poleg
-ponavadi
-ponovno
-potem
-povsod
-prbl.
-precej
-pred
-prej
-preko
-pri
-pribl.
-približno
-proti
-r
-redko
-res
-s
-saj
-sam
-sama
-same
-sami
-samo
-se
-sebe
-sebi
-sedaj
-sem
-seveda
-si
-sicer
-skoraj
-skozi
-smo
-so
-spet
-sta
-ste
-sva
-t
-ta
-tak
-taka
-take
-taki
-tako
-takoj
-tam
-te
-tebe
-tebi
-tega
-ti
-tista
-tiste
-tisti
-tisto
-tj.
-tja
-to
-toda
-tu
-tudi
-tukaj
-tvoj
-tvoja
-tvoje
 u
-v
+
-vaju
+v vaju vam vas vaš vaša vaše ve vedno velik velika veliki
-vam
+veliko vendar ves več vi vidva vii viii visok visoka visoke
-vas
+visoki vsa vsaj vsak vsaka vsakdo vsake vsaki vsakomur vse
-vaš
+vsega vsi vso včasih včeraj 
-vaša
+
-vaše
-ve
-vedno
-vendar
-ves
-več
-vi
-vidva
-vii
-viii
-vsa
-vsaj
-vsak
-vsaka
-vsakdo
-vsake
-vsaki
-vsakomur
-vse
-vsega
-vsi
-vso
-včasih
 x 
-z
+
-za
+z za zadaj zadnji zakaj zaprta zaprti zaprto zdaj zelo zunaj
-zadaj
+
-zadnji
+ž že
-zakaj
-zdaj
-zelo
-zunaj
-č
-če
-često
-čez
-čigav
-š
-ž
-že
 """.split()
 )

spacy/lang/sl/tokenizer_exceptions.py

@@ -0,0 +1,272 @@
+from typing import Dict, List
+from ..tokenizer_exceptions import BASE_EXCEPTIONS
+from ...symbols import ORTH, NORM
+from ...util import update_exc
+
+_exc: Dict[str, List[Dict]] = {}
+
+_other_exc = {
+    "t.i.": [{ORTH: "t.", NORM: "tako"}, {ORTH: "i.", NORM: "imenovano"}],
+    "t.j.": [{ORTH: "t.", NORM: "to"}, {ORTH: "j.", NORM: "je"}],
+    "T.j.": [{ORTH: "T.", NORM: "to"}, {ORTH: "j.", NORM: "je"}],
+    "d.o.o.": [
+        {ORTH: "d.", NORM: "družba"},
+        {ORTH: "o.", NORM: "omejeno"},
+        {ORTH: "o.", NORM: "odgovornostjo"},
+    ],
+    "D.O.O.": [
+        {ORTH: "D.", NORM: "družba"},
+        {ORTH: "O.", NORM: "omejeno"},
+        {ORTH: "O.", NORM: "odgovornostjo"},
+    ],
+    "d.n.o.": [
+        {ORTH: "d.", NORM: "družba"},
+        {ORTH: "n.", NORM: "neomejeno"},
+        {ORTH: "o.", NORM: "odgovornostjo"},
+    ],
+    "D.N.O.": [
+        {ORTH: "D.", NORM: "družba"},
+        {ORTH: "N.", NORM: "neomejeno"},
+        {ORTH: "O.", NORM: "odgovornostjo"},
+    ],
+    "d.d.": [{ORTH: "d.", NORM: "delniška"}, {ORTH: "d.", NORM: "družba"}],
+    "D.D.": [{ORTH: "D.", NORM: "delniška"}, {ORTH: "D.", NORM: "družba"}],
+    "s.p.": [{ORTH: "s.", NORM: "samostojni"}, {ORTH: "p.", NORM: "podjetnik"}],
+    "S.P.": [{ORTH: "S.", NORM: "samostojni"}, {ORTH: "P.", NORM: "podjetnik"}],
+    "l.r.": [{ORTH: "l.", NORM: "lastno"}, {ORTH: "r.", NORM: "ročno"}],
+    "le-te": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "te"}],
+    "Le-te": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "te"}],
+    "le-ti": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "ti"}],
+    "Le-ti": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "ti"}],
+    "le-to": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "to"}],
+    "Le-to": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "to"}],
+    "le-ta": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "ta"}],
+    "Le-ta": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "ta"}],
+    "le-tega": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "tega"}],
+    "Le-tega": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "tega"}],
+}
+
+_exc.update(_other_exc)
+
+
+for exc_data in [
+    {ORTH: "adm.", NORM: "administracija"},
+    {ORTH: "aer.", NORM: "aeronavtika"},
+    {ORTH: "agr.", NORM: "agronomija"},
+    {ORTH: "amer.", NORM: "ameriško"},
+    {ORTH: "anat.", NORM: "anatomija"},
+    {ORTH: "angl.", NORM: "angleški"},
+    {ORTH: "ant.", NORM: "antonim"},
+    {ORTH: "antr.", NORM: "antropologija"},
+    {ORTH: "apr.", NORM: "april"},
+    {ORTH: "arab.", NORM: "arabsko"},
+    {ORTH: "arheol.", NORM: "arheologija"},
+    {ORTH: "arhit.", NORM: "arhitektura"},
+    {ORTH: "avg.", NORM: "avgust"},
+    {ORTH: "avstr.", NORM: "avstrijsko"},
+    {ORTH: "avt.", NORM: "avtomobilizem"},
+    {ORTH: "bibl.", NORM: "biblijsko"},
+    {ORTH: "biokem.", NORM: "biokemija"},
+    {ORTH: "biol.", NORM: "biologija"},
+    {ORTH: "bolg.", NORM: "bolgarski"},
+    {ORTH: "bot.", NORM: "botanika"},
+    {ORTH: "cit.", NORM: "citat"},
+    {ORTH: "daj.", NORM: "dajalnik"},
+    {ORTH: "del.", NORM: "deležnik"},
+    {ORTH: "ed.", NORM: "ednina"},
+    {ORTH: "etn.", NORM: "etnografija"},
+    {ORTH: "farm.", NORM: "farmacija"},
+    {ORTH: "filat.", NORM: "filatelija"},
+    {ORTH: "filoz.", NORM: "filozofija"},
+    {ORTH: "fin.", NORM: "finančništvo"},
+    {ORTH: "fiz.", NORM: "fizika"},
+    {ORTH: "fot.", NORM: "fotografija"},
+    {ORTH: "fr.", NORM: "francoski"},
+    {ORTH: "friz.", NORM: "frizerstvo"},
+    {ORTH: "gastr.", NORM: "gastronomija"},
+    {ORTH: "geogr.", NORM: "geografija"},
+    {ORTH: "geol.", NORM: "geologija"},
+    {ORTH: "geom.", NORM: "geometrija"},
+    {ORTH: "germ.", NORM: "germanski"},
+    {ORTH: "gl.", NORM: "glej"},
+    {ORTH: "glag.", NORM: "glagolski"},
+    {ORTH: "glasb.", NORM: "glasba"},
+    {ORTH: "gled.", NORM: "gledališče"},
+    {ORTH: "gost.", NORM: "gostinstvo"},
+    {ORTH: "gozd.", NORM: "gozdarstvo"},
+    {ORTH: "gr.", NORM: "grški"},
+    {ORTH: "grad.", NORM: "gradbeništvo"},
+    {ORTH: "hebr.", NORM: "hebrejsko"},
+    {ORTH: "hrv.", NORM: "hrvaško"},
+    {ORTH: "ide.", NORM: "indoevropsko"},
+    {ORTH: "igr.", NORM: "igre"},
+    {ORTH: "im.", NORM: "imenovalnik"},
+    {ORTH: "iron.", NORM: "ironično"},
+    {ORTH: "it.", NORM: "italijanski"},
+    {ORTH: "itd.", NORM: "in tako dalje"},
+    {ORTH: "itn.", NORM: "in tako naprej"},
+    {ORTH: "ipd.", NORM: "in podobno"},
+    {ORTH: "jap.", NORM: "japonsko"},
+    {ORTH: "jul.", NORM: "julij"},
+    {ORTH: "jun.", NORM: "junij"},
+    {ORTH: "kit.", NORM: "kitajsko"},
+    {ORTH: "knj.", NORM: "knjižno"},
+    {ORTH: "knjiž.", NORM: "knjižno"},
+    {ORTH: "kor.", NORM: "koreografija"},
+    {ORTH: "lat.", NORM: "latinski"},
+    {ORTH: "les.", NORM: "lesna stroka"},
+    {ORTH: "lingv.", NORM: "lingvistika"},
+    {ORTH: "lit.", NORM: "literarni"},
+    {ORTH: "ljubk.", NORM: "ljubkovalno"},
+    {ORTH: "lov.", NORM: "lovstvo"},
+    {ORTH: "m.", NORM: "moški"},
+    {ORTH: "mak.", NORM: "makedonski"},
+    {ORTH: "mar.", NORM: "marec"},
+    {ORTH: "mat.", NORM: "matematika"},
+    {ORTH: "med.", NORM: "medicina"},
+    {ORTH: "meh.", NORM: "mehiško"},
+    {ORTH: "mest.", NORM: "mestnik"},
+    {ORTH: "mdr.", NORM: "med drugim"},
+    {ORTH: "min.", NORM: "mineralogija"},
+    {ORTH: "mitol.", NORM: "mitologija"},
+    {ORTH: "mn.", NORM: "množina"},
+    {ORTH: "mont.", NORM: "montanistika"},
+    {ORTH: "muz.", NORM: "muzikologija"},
+    {ORTH: "nam.", NORM: "namenilnik"},
+    {ORTH: "nar.", NORM: "narečno"},
+    {ORTH: "nav.", NORM: "navadno"},
+    {ORTH: "nedol.", NORM: "nedoločnik"},
+    {ORTH: "nedov.", NORM: "nedovršni"},
+    {ORTH: "neprav.", NORM: "nepravilno"},
+    {ORTH: "nepreh.", NORM: "neprehodno"},
+    {ORTH: "neskl.", NORM: "nesklonljiv(o)"},
+    {ORTH: "nestrok.", NORM: "nestrokovno"},
+    {ORTH: "num.", NORM: "numizmatika"},
+    {ORTH: "npr.", NORM: "na primer"},
+    {ORTH: "obrt.", NORM: "obrtništvo"},
+    {ORTH: "okt.", NORM: "oktober"},
+    {ORTH: "or.", NORM: "orodnik"},
+    {ORTH: "os.", NORM: "oseba"},
+    {ORTH: "otr.", NORM: "otroško"},
+    {ORTH: "oz.", NORM: "oziroma"},
+    {ORTH: "pal.", NORM: "paleontologija"},
+    {ORTH: "papir.", NORM: "papirništvo"},
+    {ORTH: "ped.", NORM: "pedagogika"},
+    {ORTH: "pisar.", NORM: "pisarniško"},
+    {ORTH: "pog.", NORM: "pogovorno"},
+    {ORTH: "polit.", NORM: "politika"},
+    {ORTH: "polj.", NORM: "poljsko"},
+    {ORTH: "poljud.", NORM: "poljudno"},
+    {ORTH: "preg.", NORM: "pregovor"},
+    {ORTH: "preh.", NORM: "prehodno"},
+    {ORTH: "pren.", NORM: "preneseno"},
+    {ORTH: "prid.", NORM: "pridevnik"},
+    {ORTH: "prim.", NORM: "primerjaj"},
+    {ORTH: "prisl.", NORM: "prislov"},
+    {ORTH: "psih.", NORM: "psihologija"},
+    {ORTH: "psiht.", NORM: "psihiatrija"},
+    {ORTH: "rad.", NORM: "radiotehnika"},
+    {ORTH: "rač.", NORM: "računalništvo"},
+    {ORTH: "rib.", NORM: "ribištvo"},
+    {ORTH: "rod.", NORM: "rodilnik"},
+    {ORTH: "rus.", NORM: "rusko"},
+    {ORTH: "s.", NORM: "srednji"},
+    {ORTH: "sam.", NORM: "samostalniški"},
+    {ORTH: "sed.", NORM: "sedanjik"},
+    {ORTH: "sep.", NORM: "september"},
+    {ORTH: "slabš.", NORM: "slabšalno"},
+    {ORTH: "slovan.", NORM: "slovansko"},
+    {ORTH: "slovaš.", NORM: "slovaško"},
+    {ORTH: "srb.", NORM: "srbsko"},
+    {ORTH: "star.", NORM: "starinsko"},
+    {ORTH: "stil.", NORM: "stilno"},
+    {ORTH: "sv.", NORM: "svet(i)"},
+    {ORTH: "teh.", NORM: "tehnika"},
+    {ORTH: "tisk.", NORM: "tiskarstvo"},
+    {ORTH: "tj.", NORM: "to je"},
+    {ORTH: "tož.", NORM: "tožilnik"},
+    {ORTH: "trg.", NORM: "trgovina"},
+    {ORTH: "ukr.", NORM: "ukrajinski"},
+    {ORTH: "um.", NORM: "umetnost"},
+    {ORTH: "vel.", NORM: "velelnik"},
+    {ORTH: "vet.", NORM: "veterina"},
+    {ORTH: "vez.", NORM: "veznik"},
+    {ORTH: "vn.", NORM: "visokonemško"},
+    {ORTH: "voj.", NORM: "vojska"},
+    {ORTH: "vrtn.", NORM: "vrtnarstvo"},
+    {ORTH: "vulg.", NORM: "vulgarno"},
+    {ORTH: "vznes.", NORM: "vzneseno"},
+    {ORTH: "zal.", NORM: "založništvo"},
+    {ORTH: "zastar.", NORM: "zastarelo"},
+    {ORTH: "zgod.", NORM: "zgodovina"},
+    {ORTH: "zool.", NORM: "zoologija"},
+    {ORTH: "čeb.", NORM: "čebelarstvo"},
+    {ORTH: "češ.", NORM: "češki"},
+    {ORTH: "člov.", NORM: "človeškost"},
+    {ORTH: "šah.", NORM: "šahovski"},
+    {ORTH: "šalj.", NORM: "šaljivo"},
+    {ORTH: "šp.", NORM: "španski"},
+    {ORTH: "špan.", NORM: "špansko"},
+    {ORTH: "šport.", NORM: "športni"},
+    {ORTH: "štev.", NORM: "števnik"},
+    {ORTH: "šved.", NORM: "švedsko"},
+    {ORTH: "švic.", NORM: "švicarsko"},
+    {ORTH: "ž.", NORM: "ženski"},
+    {ORTH: "žarg.", NORM: "žargonsko"},
+    {ORTH: "žel.", NORM: "železnica"},
+    {ORTH: "živ.", NORM: "živost"},
+]:
+    _exc[exc_data[ORTH]] = [exc_data]
+
+
+abbrv = """
+Co. Ch. DIPL. DR. Dr. Ev. Inc. Jr. Kr. Mag. M. MR. Mr. Mt. Murr. Npr. OZ. 
+Opr. Osn. Prim. Roj. ST. Sim. Sp. Sred. St. Sv. Škofl. Tel. UR. Zb. 
+a. aa. ab. abc. abit. abl. abs. abt. acc. accel. add. adj. adv. aet. afr. akad. al. alban. all. alleg. 
+alp. alt. alter. alžir. am. an. andr. ang. anh. anon. ans. antrop. apoc. app. approx. apt. ar. arc. arch. 
+arh. arr. as. asist. assist. assoc. asst. astr. attn. aug. avstral. az. b. bab. bal. bbl. bd. belg. bioinf. 
+biomed. bk. bl. bn. borg. bp. br. braz. brit. bros. broš. bt. bu. c. ca. cal. can. cand. cantab. cap. capt.
+cat. cath. cc. cca. cd. cdr. cdre. cent. cerkv. cert. cf. cfr. ch. chap. chem. chr. chs. cic. circ. civ. cl.
+cm. cmd. cnr. co. cod. col. coll. colo. com. comp. con. conc. cond. conn. cons. cont. coop. corr. cost. cp.
+cpl. cr. crd. cres. cresc. ct. cu. d. dan. dat. davč. ddr. dec. ded. def. dem. dent. dept. dia. dip. dipl. 
+dir. disp. diss. div. do. doc. dok. dol. doo. dop. dott. dr. dram. druž. družb. drž. dt. duh. dur. dvr. dwt. e.
+ea. ecc. eccl. eccles. econ. edn. egipt. egr. ekon. eksp. el. em. enc. eng. eo. ep. err. esp. esq. est.
+et. etc. etnogr. etnol. ev. evfem. evr. ex. exc. excl. exp. expl. ext. exx. f. fa. facs. fak. faks. fas.
+fasc. fco. fcp. feb. febr. fec. fed. fem. ff. fff. fid. fig. fil. film. fiziol. fiziot. flam. fm. fo. fol. folk.
+frag. fran. franc. fsc. g. ga. gal. gdč. ge. gen. geod. geog. geotehnol. gg. gimn. glas. glav. gnr. go. gor.
+gosp. gp. graf. gram. gren. grš. gs. h. hab. hf. hist. ho. hort. i. ia. ib. ibid. id. idr. idridr. ill. imen.
+imp. impf. impr. in. inc. incl. ind. indus. inf. inform. ing. init. ins. int. inv. inšp. inštr. inž. is. islam.
+ist. ital. iur. iz. izbr. izd. izg. izgr. izr. izv. j. jak. jam. jan. jav. je. jez. jr. jsl. jud. jug.
+jugoslovan. jur. juž. jv. jz. k. kal. kan. kand. kat. kdo. kem. kip. kmet. kol. kom. komp. konf. kont. kost. kov. 
+kp. kpfw. kr. kraj. krat. kub. kult. kv. kval. l. la. lab. lb. ld. let. lib. lik. litt. lj. ljud. ll. loc. log. 
+loč. lt. ma. madž. mag. manag. manjš. masc. mass. mater. max. maxmax. mb. md. mech. medic. medij. medn. 
+mehč. mem. menedž. mes. mess. metal. meteor. meteorol. mex. mi. mikr. mil. minn. mio. misc. miss. mit. mk. 
+mkt. ml. mlad. mlle. mlr. mm. mme. množ. mo. moj. moš. možn. mr. mrd. mrs. ms. msc. msgr. mt. murr. mus. mut. 
+n. na. nad. nadalj. nadom. nagl. nakl. namer. nan. naniz. nasl. nat. navt. nač. ned. nem. nik. nizoz. nm. nn. 
+no. nom. norv. notr. nov. novogr. ns. o. ob. obd. obj. oblač. obl. oblik. obr. obraz. obs. obst. obt. obč. oc. 
+oct. od. odd. odg. odn. odst. odv. oec. off. ok. okla. okr. ont. oo. op. opis. opp. opr. orch. ord. ore. oreg. 
+org. orient. orig. ork. ort. oseb. osn. ot. ozir. ošk. p. pag. par. para. parc. parl. part. past. pat. pdk. 
+pen. perf. pert. perz. pesn. pet. pev. pf. pfc. ph. pharm. phil. pis. pl. po. pod. podr. podaljš. pogl. pogoj. pojm. 
+pok. pokr. pol. poljed. poljub. polu. pom. pomen. pon. ponov. pop. por. port. pos. posl. posn. pov. pp. ppl. pr. 
+praet. prav. pravopis. pravosl. preb. pred. predl. predm. predp. preds. pref. pregib. prel. prem. premen. prep. 
+pres. pret. prev. pribl. prih. pril. primerj. primor. prip. pripor. prir. prist. priv. proc. prof. prog. proiz. 
+prom. pron. prop. prot. protest. prov. ps. pss. pt. publ. pz. q. qld. qu. quad. que. r. racc. rastl. razgl. 
+razl. razv. rd. red. ref. reg. rel. relig. rep. repr. rer. resp. rest. ret. rev. revol. rež. rim. rist. rkp. rm. 
+roj. rom. romun. rp. rr. rt. rud. ruš. ry. sal. samogl. san. sc. scen. sci. scr. sdv. seg. sek. sen. sept. ser. 
+sev. sg. sgt. sh. sig. sigg. sign. sim. sin. sing. sinh. skand. skl. sklad. sklanj. sklep. skr. sl. slik. slov. 
+slovak. slovn. sn. so. sob. soc. sociol. sod. sopomen. sopr. sor. sov. sovj. sp. spec. spl. spr. spreg. sq. sr. 
+sre. sred. sredoz. srh. ss. ssp. st. sta. stan. stanstar. stcsl. ste. stim. stol. stom. str. stroj. strok. stsl. 
+stud. sup. supl. suppl. svet. sz. t. tab. tech. ted. tehn. tehnol. tek. teks. tekst. tel. temp. ten. teol. ter. 
+term. test. th. theol. tim. tip. tisočl. tit. tl. tol. tolmač. tom. tor. tov. tr. trad. traj. trans. tren. 
+trib. tril. trop. trp. trž. ts. tt. tu. tur. turiz. tvor. tvorb. tč. u. ul. umet. un. univ. up. upr. ur. urad. 
+us. ust. utr. v. va. val. var. varn. ven. ver. verb. vest. vezal. vic. vis. viv. viz. viš. vod. vok. vol. vpr. 
+vrst. vrstil. vs. vv. vzd. vzg. vzh. vzor. w. wed. wg. wk. x. y. z. zah. zaim. zak. zap. zasl. zavar. zač. zb. 
+združ. zg. zn. znan. znanstv. zoot. zun. zv. zvd. á. é. ć. č. čas. čet. čl. člen. čustv. đ. ľ. ł. ş. ŠT. š. šir. 
+škofl. škot. šol. št. števil. štud. ů. ű. žen. žival. 
+""".split()
+
+for orth in abbrv:
+    _exc[orth] = [{ORTH: orth}]
+
+
+TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)

spacy/lang/uk/__init__.py

@@ -29,7 +29,7 @@ class Ukrainian(Language):
     assigns=["token.lemma"],
     default_config={
         "model": None,
-        "mode": "pymorphy2",
+        "mode": "pymorphy3",
         "overwrite": False,
         "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
     },

spacy/lang/uk/lemmatizer.py

@@ -14,7 +14,7 @@ class UkrainianLemmatizer(RussianLemmatizer):
         model: Optional[Model],
         name: str = "lemmatizer",
         *,
-        mode: str = "pymorphy2",
+        mode: str = "pymorphy3",
         overwrite: bool = False,
         scorer: Optional[Callable] = lemmatizer_score,
     ) -> None:
@@ -29,6 +29,17 @@ class UkrainianLemmatizer(RussianLemmatizer):
                 ) from None
             if getattr(self, "_morph", None) is None:
                 self._morph = MorphAnalyzer(lang="uk")
+        elif mode == "pymorphy3":
+            try:
+                from pymorphy3 import MorphAnalyzer
+            except ImportError:
+                raise ImportError(
+                    "The Ukrainian lemmatizer mode 'pymorphy3' requires the "
+                    "pymorphy3 library and dictionaries. Install them with: "
+                    "pip install pymorphy3 pymorphy3-dicts-uk"
+                ) from None
+            if getattr(self, "_morph", None) is None:
+                self._morph = MorphAnalyzer(lang="uk")
         super().__init__(
             vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
         )

spacy/language.py

@@ -1879,31 +1879,22 @@ class Language:
         if isinstance(exclude, str):
             exclude = [exclude]
 
-        def fetch_pipes_status(value: Iterable[str], key: str) -> Iterable[str]:
+        # `enable` should not be merged with `enabled` (the opposite is true for `disable`/`disabled`). If the config
-            """Fetch value for `enable` or `disable` w.r.t. the specified config and passed arguments passed to
+        # specifies values for `enabled` not included in `enable`, emit warning.
-            .load(). If both arguments and config specified values for this field, the passed arguments take precedence
+        if id(enable) != id(_DEFAULT_EMPTY_PIPES):
-            and a warning is printed.
+            enabled = config["nlp"].get("enabled", [])
-            value (Iterable[str]): Passed value for `enable` or `disable`.
+            if len(enabled) and not set(enabled).issubset(enable):
-            key (str): Key for field in config (either "enabled" or "disabled").
-            RETURN (Iterable[str]):
-            """
-            # We assume that no argument was passed if the value is the specified default value.
-            if id(value) == id(_DEFAULT_EMPTY_PIPES):
-                return config["nlp"].get(key, [])
-            else:
-                if len(config["nlp"].get(key, [])):
                 warnings.warn(
                     Warnings.W123.format(
-                            arg=key[:-1],
+                        enable=enable,
-                            arg_value=value,
+                        enabled=enabled,
-                            config_value=config["nlp"][key],
                     )
                 )
-                return value
 
+        # Ensure sets of disabled/enabled pipe names are not contradictory.
         disabled_pipes = cls._resolve_component_status(
-            fetch_pipes_status(disable, "disabled"),
+            list({*disable, *config["nlp"].get("disabled", [])}),
-            fetch_pipes_status(enable, "enabled"),
+            enable,
             config["nlp"]["pipeline"],
         )
         nlp._disabled = set(p for p in disabled_pipes if p not in exclude)
@@ -2084,10 +2075,12 @@ class Language:
         if enable:
             if isinstance(enable, str):
                 enable = [enable]
-            to_disable = [
+            to_disable = {
-                pipe_name for pipe_name in pipe_names if pipe_name not in enable
+                *[pipe_name for pipe_name in pipe_names if pipe_name not in enable],
-            ]
+                *disable,
-            if disable and disable != to_disable:
+            }
+            # If any pipe to be enabled is in to_disable, the specification is inconsistent.
+            if len(set(enable) & to_disable):
                 raise ValueError(Errors.E1042.format(enable=enable, disable=disable))
 
         return tuple(to_disable)

spacy/ml/models/entity_linker.py

@@ -1,11 +1,12 @@
 from pathlib import Path
 from typing import Optional, Callable, Iterable, List, Tuple
 from thinc.types import Floats2d
-from thinc.api import chain, clone, list2ragged, reduce_mean, residual
+from thinc.api import chain, list2ragged, reduce_mean, residual
-from thinc.api import Model, Maxout, Linear, noop, tuplify, Ragged
+from thinc.api import Model, Maxout, Linear, tuplify, Ragged
 
 from ...util import registry
-from ...kb import KnowledgeBase, Candidate, get_candidates
+from ...kb import KnowledgeBase, InMemoryLookupKB
+from ...kb import Candidate, get_candidates, get_candidates_batch
 from ...vocab import Vocab
 from ...tokens import Span, Doc
 from ..extract_spans import extract_spans
@@ -70,17 +71,18 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
             cands.append((start_token, end_token))
 
         candidates.append(ops.asarray2i(cands))
-    candlens = ops.asarray1i([len(cands) for cands in candidates])
+    lengths = model.ops.asarray1i([len(cands) for cands in candidates])
-    candidates = ops.xp.concatenate(candidates)
+    out = Ragged(model.ops.flatten(candidates), lengths)
-    outputs = Ragged(candidates, candlens)
     # because this is just rearranging docs, the backprop does nothing
-    return outputs, lambda x: []
+    return out, lambda x: []
 
 
 @registry.misc("spacy.KBFromFile.v1")
-def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
+def load_kb(
-    def kb_from_file(vocab):
+    kb_path: Path,
-        kb = KnowledgeBase(vocab, entity_vector_length=1)
+) -> Callable[[Vocab], KnowledgeBase]:
+    def kb_from_file(vocab: Vocab):
+        kb = InMemoryLookupKB(vocab, entity_vector_length=1)
         kb.from_disk(kb_path)
         return kb
 
@@ -88,9 +90,11 @@ def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
 
 
 @registry.misc("spacy.EmptyKB.v1")
-def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
+def empty_kb(
-    def empty_kb_factory(vocab):
+    entity_vector_length: int,
-        return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
+) -> Callable[[Vocab], KnowledgeBase]:
+    def empty_kb_factory(vocab: Vocab):
+        return InMemoryLookupKB(vocab=vocab, entity_vector_length=entity_vector_length)
 
     return empty_kb_factory
 
@@ -98,3 +102,10 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
 @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]]
+]:
+    return get_candidates_batch

spacy/pipeline/entity_linker.py

@@ -53,9 +53,11 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
         "incl_context": True,
         "entity_vector_length": 64,
         "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
+        "get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
         "overwrite": True,
         "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
         "use_gold_ents": True,
+        "candidates_batch_size": 1,
         "threshold": None,
     },
     default_score_weights={
@@ -75,9 +77,13 @@ def make_entity_linker(
     incl_context: bool,
     entity_vector_length: int,
     get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
+    get_candidates_batch: Callable[
+        [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
+    ],
     overwrite: bool,
     scorer: Optional[Callable],
     use_gold_ents: bool,
+    candidates_batch_size: int,
     threshold: Optional[float] = None,
 ):
     """Construct an EntityLinker component.
@@ -90,17 +96,21 @@ def make_entity_linker(
     incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
     incl_context (bool): Whether or not to include the local context in the model.
     entity_vector_length (int): Size of encoding vectors in the KB.
-    get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
+    get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
         produces a list of candidates, given a certain knowledge base and a textual mention.
+    get_candidates_batch (
+        Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
+        ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
     scorer (Optional[Callable]): The scoring method.
     use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
         component must provide entity annotations.
+    candidates_batch_size (int): Size of batches for entity candidate generation.
     threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
         prediction is discarded. If None, predictions are not filtered by any threshold.
     """
 
     if not model.attrs.get("include_span_maker", False):
-        # The only difference in arguments here is that use_gold_ents is not available
+        # The only difference in arguments here is that use_gold_ents and threshold aren't available.
         return EntityLinker_v1(
             nlp.vocab,
             model,
@@ -124,9 +134,11 @@ def make_entity_linker(
         incl_context=incl_context,
         entity_vector_length=entity_vector_length,
         get_candidates=get_candidates,
+        get_candidates_batch=get_candidates_batch,
         overwrite=overwrite,
         scorer=scorer,
         use_gold_ents=use_gold_ents,
+        candidates_batch_size=candidates_batch_size,
         threshold=threshold,
     )
 
@@ -160,9 +172,13 @@ class EntityLinker(TrainablePipe):
         incl_context: bool,
         entity_vector_length: int,
         get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
+        get_candidates_batch: Callable[
+            [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
+        ],
         overwrite: bool = BACKWARD_OVERWRITE,
         scorer: Optional[Callable] = entity_linker_score,
         use_gold_ents: bool,
+        candidates_batch_size: int,
         threshold: Optional[float] = None,
     ) -> None:
         """Initialize an entity linker.
@@ -178,10 +194,14 @@ class EntityLinker(TrainablePipe):
         entity_vector_length (int): Size of encoding vectors in the KB.
         get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
             produces a list of candidates, given a certain knowledge base and a textual mention.
-        scorer (Optional[Callable]): The scoring method. Defaults to
+        get_candidates_batch (
-            Scorer.score_links.
+            Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
+            Iterable[Candidate]]
+            ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
+        scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
         use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
             component must provide entity annotations.
+        candidates_batch_size (int): Size of batches for entity candidate generation.
         threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the
             threshold, prediction is discarded. If None, predictions are not filtered by any threshold.
         DOCS: https://spacy.io/api/entitylinker#init
@@ -204,22 +224,27 @@ class EntityLinker(TrainablePipe):
         self.incl_prior = incl_prior
         self.incl_context = incl_context
         self.get_candidates = get_candidates
+        self.get_candidates_batch = get_candidates_batch
         self.cfg: Dict[str, Any] = {"overwrite": overwrite}
         self.distance = CosineDistance(normalize=False)
         # how many neighbour sentences to take into account
-        # create an empty KB by default. If you want to load a predefined one, specify it in 'initialize'.
+        # create an empty KB by default
         self.kb = empty_kb(entity_vector_length)(self.vocab)
         self.scorer = scorer
         self.use_gold_ents = use_gold_ents
+        self.candidates_batch_size = candidates_batch_size
         self.threshold = threshold
 
+        if candidates_batch_size < 1:
+            raise ValueError(Errors.E1044)
+
     def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
         """Define the KB of this pipe by providing a function that will
         create it using this object's vocab."""
         if not callable(kb_loader):
             raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))
 
-        self.kb = kb_loader(self.vocab)
+        self.kb = kb_loader(self.vocab)  # type: ignore
 
     def validate_kb(self) -> None:
         # Raise an error if the knowledge base is not initialized.
@@ -241,8 +266,8 @@ class EntityLinker(TrainablePipe):
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
         nlp (Language): The current nlp object the component is part of.
-        kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
+        kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab
-            Note that providing this argument, will overwrite all data accumulated in the current KB.
+            instance. Note that providing this argument will overwrite all data accumulated in the current KB.
             Use this only when loading a KB as-such from file.
 
         DOCS: https://spacy.io/api/entitylinker#initialize
@@ -419,15 +444,40 @@ class EntityLinker(TrainablePipe):
             if len(doc) == 0:
                 continue
             sentences = [s for s in doc.sents]
-            # Looping through each entity (TODO: rewrite)
+
-            for ent in doc.ents:
+            # Loop over entities in batches.
+            for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
+                ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]
+
+                # Look up candidate entities.
+                valid_ent_idx = [
+                    idx
+                    for idx in range(len(ent_batch))
+                    if ent_batch[idx].label_ not in self.labels_discard
+                ]
+
+                batch_candidates = list(
+                    self.get_candidates_batch(
+                        self.kb, [ent_batch[idx] for idx in valid_ent_idx]
+                    )
+                    if self.candidates_batch_size > 1
+                    else [
+                        self.get_candidates(self.kb, ent_batch[idx])
+                        for idx in valid_ent_idx
+                    ]
+                )
+
+                # Looping through each entity in batch (TODO: rewrite)
+                for j, ent in enumerate(ent_batch):
                     sent_index = sentences.index(ent.sent)
                     assert sent_index >= 0
 
                     if self.incl_context:
                         # get n_neighbour sentences, clipped to the length of the document
                         start_sentence = max(0, sent_index - self.n_sents)
-                    end_sentence = min(len(sentences) - 1, sent_index + self.n_sents)
+                        end_sentence = min(
+                            len(sentences) - 1, sent_index + self.n_sents
+                        )
                         start_token = sentences[start_sentence].start
                         end_token = sentences[end_sentence].end
                         sent_doc = doc[start_token:end_token].as_doc()
@@ -440,7 +490,7 @@ class EntityLinker(TrainablePipe):
                         # ignoring this entity - setting to NIL
                         final_kb_ids.append(self.NIL)
                     else:
-                    candidates = list(self.get_candidates(self.kb, ent))
+                        candidates = list(batch_candidates[j])
                         if not candidates:
                             # no prediction possible for this entity - setting to NIL
                             final_kb_ids.append(self.NIL)
@@ -476,9 +526,11 @@ class EntityLinker(TrainablePipe):
                                 scores = prior_probs + sims - (prior_probs * sims)
                             final_kb_ids.append(
                                 candidates[scores.argmax().item()].entity_
-                            if self.threshold is None or scores.max() >= self.threshold
+                                if self.threshold is None
+                                or scores.max() >= self.threshold
                                 else EntityLinker.NIL
                             )
+
         if not (len(final_kb_ids) == entity_count):
             err = Errors.E147.format(
                 method="predict", msg="result variables not of equal length"

spacy/pipeline/legacy/entity_linker.py

@@ -68,8 +68,7 @@ class EntityLinker_v1(TrainablePipe):
         entity_vector_length (int): Size of encoding vectors in the KB.
         get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
             produces a list of candidates, given a certain knowledge base and a textual mention.
-        scorer (Optional[Callable]): The scoring method. Defaults to
+        scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
-            Scorer.score_links.
         DOCS: https://spacy.io/api/entitylinker#init
         """
         self.vocab = vocab
@@ -115,7 +114,7 @@ class EntityLinker_v1(TrainablePipe):
         get_examples (Callable[[], Iterable[Example]]): Function that
             returns a representative sample of gold-standard Example objects.
         nlp (Language): The current nlp object the component is part of.
-        kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
+        kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates an InMemoryLookupKB from a Vocab instance.
             Note that providing this argument, will overwrite all data accumulated in the current KB.
             Use this only when loading a KB as-such from file.
 

spacy/pipeline/spancat.py

@@ -26,17 +26,17 @@ scorer = {"@layers": "spacy.LinearLogistic.v1"}
 hidden_size = 128
 
 [model.tok2vec]
-@architectures = "spacy.Tok2Vec.v1"
+@architectures = "spacy.Tok2Vec.v2"
 
 [model.tok2vec.embed]
-@architectures = "spacy.MultiHashEmbed.v1"
+@architectures = "spacy.MultiHashEmbed.v2"
 width = 96
 rows = [5000, 2000, 1000, 1000]
 attrs = ["ORTH", "PREFIX", "SUFFIX", "SHAPE"]
 include_static_vectors = false
 
 [model.tok2vec.encode]
-@architectures = "spacy.MaxoutWindowEncoder.v1"
+@architectures = "spacy.MaxoutWindowEncoder.v2"
 width = ${model.tok2vec.embed.width}
 window_size = 1
 maxout_pieces = 3

spacy/pipeline/textcat.py

@@ -24,8 +24,8 @@ single_label_default_config = """
 [model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = 64
-rows = [2000, 2000, 1000, 1000, 1000, 1000]
+rows = [2000, 2000, 500, 1000, 500]
-attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
+attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
 include_static_vectors = false
 
 [model.tok2vec.encode]
@@ -72,7 +72,7 @@ subword_features = true
     "textcat",
     assigns=["doc.cats"],
     default_config={
-        "threshold": 0.5,
+        "threshold": 0.0,
         "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
         "scorer": {"@scorers": "spacy.textcat_scorer.v1"},
     },
@@ -144,7 +144,8 @@ class TextCategorizer(TrainablePipe):
         model (thinc.api.Model): The Thinc Model powering the pipeline component.
         name (str): The component instance name, used to add entries to the
             losses during training.
-        threshold (float): Cutoff to consider a prediction "positive".
+        threshold (float): Unused, not needed for single-label (exclusive
+            classes) classification.
         scorer (Optional[Callable]): The scoring method. Defaults to
                 Scorer.score_cats for the attribute "cats".
 
@@ -154,7 +155,11 @@ class TextCategorizer(TrainablePipe):
         self.model = model
         self.name = name
         self._rehearsal_model = None
-        cfg = {"labels": [], "threshold": threshold, "positive_label": None}
+        cfg: Dict[str, Any] = {
+            "labels": [],
+            "threshold": threshold,
+            "positive_label": None,
+        }
         self.cfg = dict(cfg)
         self.scorer = scorer
 

spacy/pipeline/textcat_multilabel.py

@@ -19,17 +19,17 @@ multi_label_default_config = """
 @architectures = "spacy.TextCatEnsemble.v2"
 
 [model.tok2vec]
-@architectures = "spacy.Tok2Vec.v1"
+@architectures = "spacy.Tok2Vec.v2"
 
 [model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v2"
 width = 64
-rows = [2000, 2000, 1000, 1000, 1000, 1000]
+rows = [2000, 2000, 500, 1000, 500]
-attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
+attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
 include_static_vectors = false
 
 [model.tok2vec.encode]
-@architectures = "spacy.MaxoutWindowEncoder.v1"
+@architectures = "spacy.MaxoutWindowEncoder.v2"
 width = ${model.tok2vec.embed.width}
 window_size = 1
 maxout_pieces = 3

spacy/pipeline/tok2vec.py

@@ -123,9 +123,6 @@ class Tok2Vec(TrainablePipe):
             width = self.model.get_dim("nO")
             return [self.model.ops.alloc((0, width)) for doc in docs]
         tokvecs = self.model.predict(docs)
-        batch_id = Tok2VecListener.get_batch_id(docs)
-        for listener in self.listeners:
-            listener.receive(batch_id, tokvecs, _empty_backprop)
         return tokvecs
 
     def set_annotations(self, docs: Sequence[Doc], tokvecses) -> None:
@@ -286,6 +283,17 @@ class Tok2VecListener(Model):
 def forward(model: Tok2VecListener, inputs, is_train: bool):
     """Supply the outputs from the upstream Tok2Vec component."""
     if is_train:
+        # This might occur during training when the tok2vec layer is frozen / hasn't been updated.
+        # In that case, it should be set to "annotating" so we can retrieve the embeddings from the doc.
+        if model._batch_id is None:
+            outputs = []
+            for doc in inputs:
+                if doc.tensor.size == 0:
+                    raise ValueError(Errors.E203.format(name="tok2vec"))
+                else:
+                    outputs.append(doc.tensor)
+            return outputs, _empty_backprop
+        else:
             model.verify_inputs(inputs)
             return model._outputs, model._backprop
     else:
@@ -306,7 +314,7 @@ def forward(model: Tok2VecListener, inputs, is_train: bool):
                 outputs.append(model.ops.alloc2f(len(doc), width))
             else:
                 outputs.append(doc.tensor)
-        return outputs, lambda dX: []
+        return outputs, _empty_backprop
 
 
 def _empty_backprop(dX):  # for pickling

spacy/schemas.py

@@ -519,9 +519,9 @@ class DocJSONSchema(BaseModel):
         title="Any custom data stored in the document's _ attribute",
         alias="_",
     )
-    underscore_token: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
+    underscore_token: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
         None, title="Any custom data stored in the token's _ attribute"
     )
-    underscore_span: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
+    underscore_span: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
         None, title="Any custom data stored in the span's _ attribute"
     )

spacy/scorer.py

@@ -446,7 +446,7 @@ class Scorer:
         labels (Iterable[str]): The set of possible labels. Defaults to [].
         multi_label (bool): Whether the attribute allows multiple labels.
             Defaults to True. When set to False (exclusive labels), missing
-            gold labels are interpreted as 0.0.
+            gold labels are interpreted as 0.0 and the threshold is set to 0.0.
         positive_label (str): The positive label for a binary task with
             exclusive classes. Defaults to None.
         threshold (float): Cutoff to consider a prediction "positive". Defaults
@@ -471,6 +471,8 @@ class Scorer:
         """
         if threshold is None:
             threshold = 0.5 if multi_label else 0.0
+        if not multi_label:
+            threshold = 0.0
         f_per_type = {label: PRFScore() for label in labels}
         auc_per_type = {label: ROCAUCScore() for label in labels}
         labels = set(labels)
@@ -505,11 +507,10 @@ class Scorer:
                 # Get the highest-scoring for each.
                 pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
                 gold_label, gold_score = max(gold_cats.items(), key=lambda it: it[1])
-                if pred_label == gold_label and pred_score >= threshold:
+                if pred_label == gold_label:
                     f_per_type[pred_label].tp += 1
                 else:
                     f_per_type[gold_label].fn += 1
-                    if pred_score >= threshold:
                     f_per_type[pred_label].fp += 1
             elif gold_cats:
                 gold_label, gold_score = max(gold_cats, key=lambda it: it[1])
@@ -517,7 +518,6 @@ class Scorer:
                     f_per_type[gold_label].fn += 1
             elif pred_cats:
                 pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
-                if pred_score >= threshold:
                 f_per_type[pred_label].fp += 1
         micro_prf = PRFScore()
         for label_prf in f_per_type.values():

spacy/tests/conftest.py

@@ -333,13 +333,13 @@ def ro_tokenizer():
 
 @pytest.fixture(scope="session")
 def ru_tokenizer():
-    pytest.importorskip("pymorphy2")
+    pytest.importorskip("pymorphy3")
     return get_lang_class("ru")().tokenizer
 
 
 @pytest.fixture
 def ru_lemmatizer():
-    pytest.importorskip("pymorphy2")
+    pytest.importorskip("pymorphy3")
     return get_lang_class("ru")().add_pipe("lemmatizer")
 
 
@@ -419,14 +419,14 @@ def ky_tokenizer():
 
 @pytest.fixture(scope="session")
 def uk_tokenizer():
-    pytest.importorskip("pymorphy2")
+    pytest.importorskip("pymorphy3")
     return get_lang_class("uk")().tokenizer
 
 
 @pytest.fixture
 def uk_lemmatizer():
-    pytest.importorskip("pymorphy2")
+    pytest.importorskip("pymorphy3")
-    pytest.importorskip("pymorphy2_dicts_uk")
+    pytest.importorskip("pymorphy3_dicts_uk")
     return get_lang_class("uk")().add_pipe("lemmatizer")
 
 

spacy/tests/doc/test_json_doc_conversion.py

@@ -128,7 +128,9 @@ def test_doc_to_json_with_token_span_attributes(doc):
     doc._.json_test1 = "hello world"
     doc._.json_test2 = [1, 2, 3]
     doc[0:1]._.span_test = "span_attribute"
+    doc[0:2]._.span_test = "span_attribute_2"
     doc[0]._.token_test = 117
+    doc[1]._.token_test = 118
     doc.spans["span_group"] = [doc[0:1]]
     json_doc = doc.to_json(
         underscore=["json_test1", "json_test2", "token_test", "span_test"]
@@ -139,8 +141,10 @@ def test_doc_to_json_with_token_span_attributes(doc):
     assert json_doc["_"]["json_test2"] == [1, 2, 3]
     assert "underscore_token" in json_doc
     assert "underscore_span" in json_doc
-    assert json_doc["underscore_token"]["token_test"]["value"] == 117
+    assert json_doc["underscore_token"]["token_test"][0]["value"] == 117
-    assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
+    assert json_doc["underscore_token"]["token_test"][1]["value"] == 118
+    assert json_doc["underscore_span"]["span_test"][0]["value"] == "span_attribute"
+    assert json_doc["underscore_span"]["span_test"][1]["value"] == "span_attribute_2"
     assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
     assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
 
@@ -161,8 +165,8 @@ def test_doc_to_json_with_custom_user_data(doc):
     assert json_doc["_"]["json_test"] == "hello world"
     assert "underscore_token" in json_doc
     assert "underscore_span" in json_doc
-    assert json_doc["underscore_token"]["token_test"]["value"] == 117
+    assert json_doc["underscore_token"]["token_test"][0]["value"] == 117
-    assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
+    assert json_doc["underscore_span"]["span_test"][0]["value"] == "span_attribute"
     assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
     assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
 
@@ -181,8 +185,8 @@ def test_doc_to_json_with_token_span_same_identifier(doc):
     assert json_doc["_"]["my_ext"] == "hello world"
     assert "underscore_token" in json_doc
     assert "underscore_span" in json_doc
-    assert json_doc["underscore_token"]["my_ext"]["value"] == 117
+    assert json_doc["underscore_token"]["my_ext"][0]["value"] == 117
-    assert json_doc["underscore_span"]["my_ext"]["value"] == "span_attribute"
+    assert json_doc["underscore_span"]["my_ext"][0]["value"] == "span_attribute"
     assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
     assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
 
@@ -195,10 +199,9 @@ def test_doc_to_json_with_token_attributes_missing(doc):
     doc[0]._.token_test = 117
     json_doc = doc.to_json(underscore=["span_test"])
 
-    assert "underscore_token" in json_doc
     assert "underscore_span" in json_doc
-    assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
+    assert json_doc["underscore_span"]["span_test"][0]["value"] == "span_attribute"
-    assert "token_test" not in json_doc["underscore_token"]
+    assert "underscore_token" not in json_doc
     assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
 
 
@@ -283,7 +286,9 @@ def test_json_to_doc_with_token_span_attributes(doc):
     doc._.json_test1 = "hello world"
     doc._.json_test2 = [1, 2, 3]
     doc[0:1]._.span_test = "span_attribute"
+    doc[0:2]._.span_test = "span_attribute_2"
     doc[0]._.token_test = 117
+    doc[1]._.token_test = 118
 
     json_doc = doc.to_json(
         underscore=["json_test1", "json_test2", "token_test", "span_test"]
@@ -295,7 +300,9 @@ def test_json_to_doc_with_token_span_attributes(doc):
     assert new_doc._.json_test1 == "hello world"
     assert new_doc._.json_test2 == [1, 2, 3]
     assert new_doc[0]._.token_test == 117
+    assert new_doc[1]._.token_test == 118
     assert new_doc[0:1]._.span_test == "span_attribute"
+    assert new_doc[0:2]._.span_test == "span_attribute_2"
     assert new_doc.user_data == doc.user_data
     assert new_doc.to_bytes(exclude=["user_data"]) == doc.to_bytes(
         exclude=["user_data"]
@@ -363,3 +370,12 @@ def test_json_to_doc_validation_error(doc):
     doc_json.pop("tokens")
     with pytest.raises(ValueError):
         Doc(doc.vocab).from_json(doc_json, validate=True)
+
+
+def test_to_json_underscore_doc_getters(doc):
+    def get_text_length(doc):
+        return len(doc.text)
+
+    Doc.set_extension("text_length", getter=get_text_length)
+    doc_json = doc.to_json(underscore=["text_length"])
+    assert doc_json["_"]["text_length"] == get_text_length(doc)

spacy/tests/lang/grc/test_tokenizer.py

@@ -0,0 +1,18 @@
+import pytest
+
+
+# fmt: off
+GRC_TOKEN_EXCEPTION_TESTS = [
+    ("τὸ 〈τῆς〉 φιλοσοφίας ἔργον ἔνιοί φασιν ἀπὸ ⟦βαρβάρων⟧ ἄρξαι.", ["τὸ", "〈", "τῆς", "〉", "φιλοσοφίας", "ἔργον", "ἔνιοί", "φασιν", "ἀπὸ", "⟦", "βαρβάρων", "⟧", "ἄρξαι", "."]),
+    ("τὴν δὲ τῶν Αἰγυπτίων φιλοσοφίαν εἶναι τοιαύτην περί τε †θεῶν† καὶ ὑπὲρ δικαιοσύνης.", ["τὴν", "δὲ", "τῶν", "Αἰγυπτίων", "φιλοσοφίαν", "εἶναι", "τοιαύτην", "περί", "τε", "†", "θεῶν", "†", "καὶ", "ὑπὲρ", "δικαιοσύνης", "."]),
+    ("⸏πόσις δ' Ἐρεχθεύς ἐστί μοι σεσωσμένος⸏", ["⸏", "πόσις", "δ'", "Ἐρεχθεύς", "ἐστί", "μοι", "σεσωσμένος", "⸏"]),
+    ("⸏ὔπνον ἴδωμεν⸎", ["⸏", "ὔπνον", "ἴδωμεν", "⸎"]),
+]
+# fmt: on
+
+
+@pytest.mark.parametrize("text,expected_tokens", GRC_TOKEN_EXCEPTION_TESTS)
+def test_grc_tokenizer(grc_tokenizer, text, expected_tokens):
+    tokens = grc_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/lang/sl/test_text.py

@@ -20,7 +20,6 @@ od katerih so te svoboščine odvisne,
     assert len(tokens) == 116
 
 
-@pytest.mark.xfail
 def test_ordinal_number(sl_tokenizer):
     text = "10. decembra 1948"
     tokens = sl_tokenizer(text)

spacy/tests/pipeline/test_entity_linker.py

@@ -6,9 +6,10 @@ from numpy.testing import assert_equal
 from spacy import registry, util
 from spacy.attrs import ENT_KB_ID
 from spacy.compat import pickle
-from spacy.kb import Candidate, KnowledgeBase, get_candidates
+from spacy.kb import Candidate, InMemoryLookupKB, get_candidates, KnowledgeBase
 from spacy.lang.en import English
 from spacy.ml import load_kb
+from spacy.ml.models.entity_linker import build_span_maker
 from spacy.pipeline import EntityLinker
 from spacy.pipeline.legacy import EntityLinker_v1
 from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
@@ -34,7 +35,7 @@ def assert_almost_equal(a, b):
 def test_issue4674():
     """Test that setting entities with overlapping identifiers does not mess up IO"""
     nlp = English()
-    kb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+    kb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
     vector1 = [0.9, 1.1, 1.01]
     vector2 = [1.8, 2.25, 2.01]
     with pytest.warns(UserWarning):
@@ -51,7 +52,7 @@ def test_issue4674():
             dir_path.mkdir()
         file_path = dir_path / "kb"
         kb.to_disk(str(file_path))
-        kb2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+        kb2 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
         kb2.from_disk(str(file_path))
     assert kb2.get_size_entities() == 1
 
@@ -59,9 +60,9 @@ def test_issue4674():
 @pytest.mark.issue(6730)
 def test_issue6730(en_vocab):
     """Ensure that the KB does not accept empty strings, but otherwise IO works fine."""
-    from spacy.kb import KnowledgeBase
+    from spacy.kb.kb_in_memory import InMemoryLookupKB
 
-    kb = KnowledgeBase(en_vocab, entity_vector_length=3)
+    kb = InMemoryLookupKB(en_vocab, entity_vector_length=3)
     kb.add_entity(entity="1", freq=148, entity_vector=[1, 2, 3])
 
     with pytest.raises(ValueError):
@@ -127,7 +128,7 @@ def test_issue7065_b():
 
     def create_kb(vocab):
         # create artificial KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
         mykb.add_alias(
             alias="No. 8",
@@ -190,7 +191,7 @@ def test_no_entities():
 
     def create_kb(vocab):
         # create artificial KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
         mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
         return mykb
@@ -231,7 +232,7 @@ def test_partial_links():
 
     def create_kb(vocab):
         # create artificial KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
         mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
         return mykb
@@ -263,7 +264,7 @@ def test_partial_links():
 
 def test_kb_valid_entities(nlp):
     """Test the valid construction of a KB with 3 entities and two aliases"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=19, entity_vector=[8, 4, 3])
@@ -292,7 +293,7 @@ def test_kb_valid_entities(nlp):
 
 def test_kb_invalid_entities(nlp):
     """Test the invalid construction of a KB with an alias linked to a non-existing entity"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@@ -308,7 +309,7 @@ def test_kb_invalid_entities(nlp):
 
 def test_kb_invalid_probabilities(nlp):
     """Test the invalid construction of a KB with wrong prior probabilities"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@@ -322,7 +323,7 @@ def test_kb_invalid_probabilities(nlp):
 
 def test_kb_invalid_combination(nlp):
     """Test the invalid construction of a KB with non-matching entity and probability lists"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@@ -338,7 +339,7 @@ def test_kb_invalid_combination(nlp):
 
 def test_kb_invalid_entity_vector(nlp):
     """Test the invalid construction of a KB with non-matching entity vector lengths"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=19, entity_vector=[1, 2, 3])
@@ -376,7 +377,7 @@ def test_kb_initialize_empty(nlp):
 
 def test_kb_serialize(nlp):
     """Test serialization of the KB"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
     with make_tempdir() as d:
         # normal read-write behaviour
         mykb.to_disk(d / "kb")
@@ -393,12 +394,12 @@ def test_kb_serialize(nlp):
 @pytest.mark.issue(9137)
 def test_kb_serialize_2(nlp):
     v = [5, 6, 7, 8]
-    kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
+    kb1 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
     kb1.set_entities(["E1"], [1], [v])
     assert kb1.get_vector("E1") == v
     with make_tempdir() as d:
         kb1.to_disk(d / "kb")
-        kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
+        kb2 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
         kb2.from_disk(d / "kb")
         assert kb2.get_vector("E1") == v
 
@@ -408,7 +409,7 @@ def test_kb_set_entities(nlp):
     v = [5, 6, 7, 8]
     v1 = [1, 1, 1, 0]
     v2 = [2, 2, 2, 3]
-    kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
+    kb1 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
     kb1.set_entities(["E0"], [1], [v])
     assert kb1.get_entity_strings() == ["E0"]
     kb1.set_entities(["E1", "E2"], [1, 9], [v1, v2])
@@ -417,7 +418,7 @@ def test_kb_set_entities(nlp):
     assert kb1.get_vector("E2") == v2
     with make_tempdir() as d:
         kb1.to_disk(d / "kb")
-        kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
+        kb2 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
         kb2.from_disk(d / "kb")
         assert set(kb2.get_entity_strings()) == {"E1", "E2"}
         assert kb2.get_vector("E1") == v1
@@ -428,7 +429,7 @@ def test_kb_serialize_vocab(nlp):
     """Test serialization of the KB and custom strings"""
     entity = "MyFunnyID"
     assert entity not in nlp.vocab.strings
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
     assert not mykb.contains_entity(entity)
     mykb.add_entity(entity, freq=342, entity_vector=[3])
     assert mykb.contains_entity(entity)
@@ -436,14 +437,14 @@ def test_kb_serialize_vocab(nlp):
     with make_tempdir() as d:
         # normal read-write behaviour
         mykb.to_disk(d / "kb")
-        mykb_new = KnowledgeBase(Vocab(), entity_vector_length=1)
+        mykb_new = InMemoryLookupKB(Vocab(), entity_vector_length=1)
         mykb_new.from_disk(d / "kb")
         assert entity in mykb_new.vocab.strings
 
 
 def test_candidate_generation(nlp):
     """Test correct candidate generation"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
     doc = nlp("douglas adam Adam shrubbery")
 
     douglas_ent = doc[0:1]
@@ -481,7 +482,7 @@ def test_el_pipe_configuration(nlp):
     ruler.add_patterns([pattern])
 
     def create_kb(vocab):
-        kb = KnowledgeBase(vocab, entity_vector_length=1)
+        kb = InMemoryLookupKB(vocab, entity_vector_length=1)
         kb.add_entity(entity="Q2", freq=12, entity_vector=[2])
         kb.add_entity(entity="Q3", freq=5, entity_vector=[3])
         kb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1])
@@ -500,10 +501,21 @@ def test_el_pipe_configuration(nlp):
     def get_lowercased_candidates(kb, span):
         return kb.get_alias_candidates(span.text.lower())
 
+    def get_lowercased_candidates_batch(kb, spans):
+        return [get_lowercased_candidates(kb, span) for span in spans]
+
     @registry.misc("spacy.LowercaseCandidateGenerator.v1")
-    def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
+    def create_candidates() -> Callable[
+        [InMemoryLookupKB, "Span"], Iterable[Candidate]
+    ]:
         return get_lowercased_candidates
 
+    @registry.misc("spacy.LowercaseCandidateBatchGenerator.v1")
+    def create_candidates_batch() -> Callable[
+        [InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[Candidate]]
+    ]:
+        return get_lowercased_candidates_batch
+
     # replace the pipe with a new one with with a different candidate generator
     entity_linker = nlp.replace_pipe(
         "entity_linker",
@@ -511,6 +523,9 @@ def test_el_pipe_configuration(nlp):
         config={
             "incl_context": False,
             "get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
+            "get_candidates_batch": {
+                "@misc": "spacy.LowercaseCandidateBatchGenerator.v1"
+            },
         },
     )
     entity_linker.set_kb(create_kb)
@@ -532,7 +547,7 @@ def test_nel_nsents(nlp):
 
 def test_vocab_serialization(nlp):
     """Test that string information is retained across storage"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@@ -552,7 +567,7 @@ def test_vocab_serialization(nlp):
 
     with make_tempdir() as d:
         mykb.to_disk(d / "kb")
-        kb_new_vocab = KnowledgeBase(Vocab(), entity_vector_length=1)
+        kb_new_vocab = InMemoryLookupKB(Vocab(), entity_vector_length=1)
         kb_new_vocab.from_disk(d / "kb")
 
         candidates = kb_new_vocab.get_alias_candidates("adam")
@@ -568,7 +583,7 @@ def test_vocab_serialization(nlp):
 
 def test_append_alias(nlp):
     """Test that we can append additional alias-entity pairs"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@@ -599,7 +614,7 @@ def test_append_alias(nlp):
 @pytest.mark.filterwarnings("ignore:\\[W036")
 def test_append_invalid_alias(nlp):
     """Test that append an alias will throw an error if prior probs are exceeding 1"""
-    mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
 
     # adding entities
     mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@@ -621,7 +636,7 @@ def test_preserving_links_asdoc(nlp):
     vector_length = 1
 
     def create_kb(vocab):
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         # adding entities
         mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
         mykb.add_entity(entity="Q2", freq=8, entity_vector=[1])
@@ -701,7 +716,11 @@ TRAIN_DATA = [
     ("Russ Cochran was a member of University of Kentucky's golf team.",
         {"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}},
          "entities": [(0, 12, "PERSON"), (43, 51, "LOC")],
-         "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]})
+         "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}),
+    # having a blank instance shouldn't break things
+    ("The weather is nice today.",
+        {"links": {}, "entities": [],
+         "sent_starts": [1, -1, 0, 0, 0, 0]})
 ]
 GOLD_entities = ["Q2146908", "Q7381115", "Q7381115", "Q2146908"]
 # fmt: on
@@ -723,7 +742,7 @@ def test_overfitting_IO():
         # create artificial KB - assign same prior weight to the two russ cochran's
         # Q2146908 (Russ Cochran): American golfer
         # Q7381115 (Russ Cochran): publisher
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
         mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
         mykb.add_alias(
@@ -805,7 +824,7 @@ def test_kb_serialization():
         kb_dir = tmp_dir / "kb"
         nlp1 = English()
         assert "Q2146908" not in nlp1.vocab.strings
-        mykb = KnowledgeBase(nlp1.vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(nlp1.vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
         mykb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
         assert "Q2146908" in nlp1.vocab.strings
@@ -828,7 +847,7 @@ def test_kb_serialization():
 def test_kb_pickle():
     # Test that the KB can be pickled
     nlp = English()
-    kb_1 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+    kb_1 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
     kb_1.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
     assert not kb_1.contains_alias("Russ Cochran")
     kb_1.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
@@ -842,7 +861,7 @@ def test_kb_pickle():
 def test_nel_pickle():
     # Test that a pipeline with an EL component can be pickled
     def create_kb(vocab):
-        kb = KnowledgeBase(vocab, entity_vector_length=3)
+        kb = InMemoryLookupKB(vocab, entity_vector_length=3)
         kb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
         kb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
         return kb
@@ -864,7 +883,7 @@ def test_nel_pickle():
 def test_kb_to_bytes():
     # Test that the KB's to_bytes method works correctly
     nlp = English()
-    kb_1 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+    kb_1 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
     kb_1.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
     kb_1.add_entity(entity="Q66", freq=9, entity_vector=[1, 2, 3])
     kb_1.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
@@ -874,7 +893,7 @@ def test_kb_to_bytes():
     )
     assert kb_1.contains_alias("Russ Cochran")
     kb_bytes = kb_1.to_bytes()
-    kb_2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+    kb_2 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
     assert not kb_2.contains_alias("Russ Cochran")
     kb_2 = kb_2.from_bytes(kb_bytes)
     # check that both KBs are exactly the same
@@ -897,7 +916,7 @@ def test_kb_to_bytes():
 def test_nel_to_bytes():
     # Test that a pipeline with an EL component can be converted to bytes
     def create_kb(vocab):
-        kb = KnowledgeBase(vocab, entity_vector_length=3)
+        kb = InMemoryLookupKB(vocab, entity_vector_length=3)
         kb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
         kb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
         return kb
@@ -987,7 +1006,7 @@ def test_legacy_architectures(name, config):
         train_examples.append(Example.from_dict(doc, annotation))
 
     def create_kb(vocab):
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
         mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
         mykb.add_alias(
@@ -1054,7 +1073,7 @@ def test_no_gold_ents(patterns):
 
     def create_kb(vocab):
         # create artificial KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q613241", freq=12, entity_vector=[6, -4, 3])
         mykb.add_alias("Kirby", ["Q613241"], [0.9])
         # Placeholder
@@ -1104,7 +1123,7 @@ def test_tokenization_mismatch():
 
     def create_kb(vocab):
         # create placeholder KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
         mykb.add_entity(entity="Q613241", freq=12, entity_vector=[6, -4, 3])
         mykb.add_alias("Kirby", ["Q613241"], [0.9])
         return mykb
@@ -1121,6 +1140,12 @@ def test_tokenization_mismatch():
     nlp.evaluate(train_examples)
 
 
+def test_abstract_kb_instantiation():
+    """Test whether instantiation of abstract KB base class fails."""
+    with pytest.raises(TypeError):
+        KnowledgeBase(None, 3)
+
+
 # fmt: off
 @pytest.mark.parametrize(
     "meet_threshold,config",
@@ -1151,7 +1176,7 @@ def test_threshold(meet_threshold: bool, config: Dict[str, Any]):
 
     def create_kb(vocab):
         # create artificial KB
-        mykb = KnowledgeBase(vocab, entity_vector_length=3)
+        mykb = InMemoryLookupKB(vocab, entity_vector_length=3)
         mykb.add_entity(entity=entity_id, freq=12, entity_vector=[6, -4, 3])
         mykb.add_alias(
             alias="Mahler",
@@ -1176,3 +1201,18 @@ def test_threshold(meet_threshold: bool, config: Dict[str, Any]):
 
     assert len(doc.ents) == 1
     assert doc.ents[0].kb_id_ == entity_id if meet_threshold else EntityLinker.NIL
+
+
+def test_span_maker_forward_with_empty():
+    """The forward pass of the span maker may have a doc with no entities."""
+    nlp = English()
+    doc1 = nlp("a b c")
+    ent = doc1[0:1]
+    ent.label_ = "X"
+    doc1.ents = [ent]
+    # no entities
+    doc2 = nlp("x y z")
+
+    # just to get a model
+    span_maker = build_span_maker()
+    span_maker([doc1, doc2], False)

spacy/tests/pipeline/test_pipe_methods.py

@@ -615,20 +615,18 @@ def test_enable_disable_conflict_with_config():
 
     with make_tempdir() as tmp_dir:
         nlp.to_disk(tmp_dir)
-        # Expected to fail, as config and arguments conflict.
+        # Expected to succeed, as config and arguments do not conflict.
-        with pytest.raises(ValueError):
+        assert spacy.load(
-            spacy.load(
             tmp_dir, enable=["tagger"], config={"nlp": {"disabled": ["senter"]}}
-            )
+        ).disabled == ["senter", "sentencizer"]
         # Expected to succeed without warning due to the lack of a conflicting config option.
         spacy.load(tmp_dir, enable=["tagger"])
-        # Expected to succeed with a warning, as disable=[] should override the config setting.
+        # Expected to fail due to conflict between enable and disabled.
-        with pytest.warns(UserWarning):
+        with pytest.raises(ValueError):
             spacy.load(
                 tmp_dir,
-                enable=["tagger"],
+                enable=["senter"],
-                disable=[],
+                config={"nlp": {"disabled": ["senter", "tagger"]}},
-                config={"nlp": {"disabled": ["senter"]}},
             )
 
 

spacy/tests/pipeline/test_textcat.py

@@ -823,10 +823,10 @@ def test_textcat_loss(multi_label: bool, expected_loss: float):
     assert loss == expected_loss
 
 
-def test_textcat_threshold():
+def test_textcat_multilabel_threshold():
     # Ensure the scorer can be called with a different threshold
     nlp = English()
-    nlp.add_pipe("textcat")
+    nlp.add_pipe("textcat_multilabel")
 
     train_examples = []
     for text, annotations in TRAIN_DATA_SINGLE_LABEL:
@@ -849,7 +849,7 @@ def test_textcat_threshold():
     )
     pos_f = scores["cats_score"]
     assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
-    assert pos_f > macro_f
+    assert pos_f >= macro_f
 
 
 def test_textcat_multi_threshold():

spacy/tests/pipeline/test_tok2vec.py

@@ -230,6 +230,97 @@ def test_tok2vec_listener_callback():
     assert get_dX(Y) is not None
 
 
+def test_tok2vec_listener_overfitting():
+    """Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components"""
+    orig_config = Config().from_str(cfg_string)
+    nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
+    train_examples = []
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+
+    for i in range(50):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses, annotates=["tok2vec"])
+    assert losses["tagger"] < 0.00001
+
+    # test the trained model
+    test_text = "I like blue eggs"
+    doc = nlp(test_text)
+    assert doc[0].tag_ == "N"
+    assert doc[1].tag_ == "V"
+    assert doc[2].tag_ == "J"
+    assert doc[3].tag_ == "N"
+
+    # Also test the results are still the same after IO
+    with make_tempdir() as tmp_dir:
+        nlp.to_disk(tmp_dir)
+        nlp2 = util.load_model_from_path(tmp_dir)
+        doc2 = nlp2(test_text)
+        assert doc2[0].tag_ == "N"
+        assert doc2[1].tag_ == "V"
+        assert doc2[2].tag_ == "J"
+        assert doc2[3].tag_ == "N"
+
+
+def test_tok2vec_frozen_not_annotating():
+    """Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating"""
+    orig_config = Config().from_str(cfg_string)
+    nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
+    train_examples = []
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+
+    for i in range(2):
+        losses = {}
+        with pytest.raises(
+            ValueError, match=r"the tok2vec embedding layer is not updated"
+        ):
+            nlp.update(
+                train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"]
+            )
+
+
+def test_tok2vec_frozen_overfitting():
+    """Test that a pipeline with a frozen & annotating tok2vec can still overfit"""
+    orig_config = Config().from_str(cfg_string)
+    nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
+    train_examples = []
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+
+    for i in range(100):
+        losses = {}
+        nlp.update(
+            train_examples,
+            sgd=optimizer,
+            losses=losses,
+            exclude=["tok2vec"],
+            annotates=["tok2vec"],
+        )
+    assert losses["tagger"] < 0.0001
+
+    # test the trained model
+    test_text = "I like blue eggs"
+    doc = nlp(test_text)
+    assert doc[0].tag_ == "N"
+    assert doc[1].tag_ == "V"
+    assert doc[2].tag_ == "J"
+    assert doc[3].tag_ == "N"
+
+    # Also test the results are still the same after IO
+    with make_tempdir() as tmp_dir:
+        nlp.to_disk(tmp_dir)
+        nlp2 = util.load_model_from_path(tmp_dir)
+        doc2 = nlp2(test_text)
+        assert doc2[0].tag_ == "N"
+        assert doc2[1].tag_ == "V"
+        assert doc2[2].tag_ == "J"
+        assert doc2[3].tag_ == "N"
+
+
 def test_replace_listeners():
     orig_config = Config().from_str(cfg_string)
     nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)

spacy/tests/serialize/test_resource_warning.py

@@ -3,7 +3,7 @@ from unittest import TestCase
 import pytest
 import srsly
 from numpy import zeros
-from spacy.kb import KnowledgeBase, Writer
+from spacy.kb.kb_in_memory import InMemoryLookupKB, Writer
 from spacy.vectors import Vectors
 from spacy.language import Language
 from spacy.pipeline import TrainablePipe
@@ -71,7 +71,7 @@ def entity_linker():
     nlp = Language()
 
     def create_kb(vocab):
-        kb = KnowledgeBase(vocab, entity_vector_length=1)
+        kb = InMemoryLookupKB(vocab, entity_vector_length=1)
         kb.add_entity("test", 0.0, zeros((1, 1), dtype="f"))
         return kb
 
@@ -120,7 +120,7 @@ def test_writer_with_path_py35():
 
 def test_save_and_load_knowledge_base():
     nlp = Language()
-    kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    kb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
     with make_tempdir() as d:
         path = d / "kb"
         try:
@@ -129,7 +129,7 @@ def test_save_and_load_knowledge_base():
             pytest.fail(str(e))
 
         try:
-            kb_loaded = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+            kb_loaded = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
             kb_loaded.from_disk(path)
         except Exception as e:
             pytest.fail(str(e))

spacy/tests/serialize/test_serialize_kb.py

@@ -2,7 +2,7 @@ from typing import Callable
 
 from spacy import util
 from spacy.util import ensure_path, registry, load_model_from_config
-from spacy.kb import KnowledgeBase
+from spacy.kb.kb_in_memory import InMemoryLookupKB
 from spacy.vocab import Vocab
 from thinc.api import Config
 
@@ -22,7 +22,7 @@ def test_serialize_kb_disk(en_vocab):
             dir_path.mkdir()
         file_path = dir_path / "kb"
         kb1.to_disk(str(file_path))
-        kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
+        kb2 = InMemoryLookupKB(vocab=en_vocab, entity_vector_length=3)
         kb2.from_disk(str(file_path))
 
     # final assertions
@@ -30,7 +30,7 @@ def test_serialize_kb_disk(en_vocab):
 
 
 def _get_dummy_kb(vocab):
-    kb = KnowledgeBase(vocab, entity_vector_length=3)
+    kb = InMemoryLookupKB(vocab, entity_vector_length=3)
     kb.add_entity(entity="Q53", freq=33, entity_vector=[0, 5, 3])
     kb.add_entity(entity="Q17", freq=2, entity_vector=[7, 1, 0])
     kb.add_entity(entity="Q007", freq=7, entity_vector=[0, 0, 7])
@@ -104,7 +104,7 @@ def test_serialize_subclassed_kb():
     custom_field = 666
     """
 
-    class SubKnowledgeBase(KnowledgeBase):
+    class SubInMemoryLookupKB(InMemoryLookupKB):
         def __init__(self, vocab, entity_vector_length, custom_field):
             super().__init__(vocab, entity_vector_length)
             self.custom_field = custom_field
@@ -112,9 +112,9 @@ def test_serialize_subclassed_kb():
     @registry.misc("spacy.CustomKB.v1")
     def custom_kb(
         entity_vector_length: int, custom_field: int
-    ) -> Callable[[Vocab], KnowledgeBase]:
+    ) -> Callable[[Vocab], InMemoryLookupKB]:
         def custom_kb_factory(vocab):
-            kb = SubKnowledgeBase(
+            kb = SubInMemoryLookupKB(
                 vocab=vocab,
                 entity_vector_length=entity_vector_length,
                 custom_field=custom_field,
@@ -129,7 +129,7 @@ def test_serialize_subclassed_kb():
     nlp.initialize()
 
     entity_linker = nlp.get_pipe("entity_linker")
-    assert type(entity_linker.kb) == SubKnowledgeBase
+    assert type(entity_linker.kb) == SubInMemoryLookupKB
     assert entity_linker.kb.entity_vector_length == 342
     assert entity_linker.kb.custom_field == 666
 
@@ -139,6 +139,6 @@ def test_serialize_subclassed_kb():
         nlp2 = util.load_model_from_path(tmp_dir)
         entity_linker2 = nlp2.get_pipe("entity_linker")
         # After IO, the KB is the standard one
-        assert type(entity_linker2.kb) == KnowledgeBase
+        assert type(entity_linker2.kb) == InMemoryLookupKB
         assert entity_linker2.kb.entity_vector_length == 342
         assert not hasattr(entity_linker2.kb, "custom_field")

spacy/tests/serialize/test_serialize_pipeline.py

@@ -404,11 +404,10 @@ def test_serialize_pipeline_disable_enable():
     assert nlp3.component_names == ["ner", "tagger"]
     with make_tempdir() as d:
         nlp3.to_disk(d)
-        with pytest.warns(UserWarning):
         nlp4 = spacy.load(d, disable=["ner"])
-    assert nlp4.pipe_names == ["tagger"]
+    assert nlp4.pipe_names == []
     assert nlp4.component_names == ["ner", "tagger"]
-    assert nlp4.disabled == ["ner"]
+    assert nlp4.disabled == ["ner", "tagger"]
     with make_tempdir() as d:
         nlp.to_disk(d)
         nlp5 = spacy.load(d, exclude=["tagger"])

spacy/tests/test_cli.py

@@ -1,5 +1,6 @@
 import os
 import math
+import pkg_resources
 from random import sample
 from typing import Counter
 
@@ -25,7 +26,7 @@ from spacy.cli.download import get_compatibility, get_version
 from spacy.cli.init_config import RECOMMENDATIONS, init_config, fill_config
 from spacy.cli.package import get_third_party_dependencies
 from spacy.cli.package import _is_permitted_package_name
-from spacy.cli.project.run import run_commands
+from spacy.cli.project.run import _check_requirements, run_commands
 from spacy.cli.validate import get_model_pkgs
 from spacy.compat import is_windows
 from spacy.lang.en import English
@@ -907,3 +908,42 @@ def test_run_commands(tmp_path):
     finally:
         # restore the original cwd so other tests are unaffected
         os.chdir(cwd)
+
+
+@pytest.mark.parametrize(
+    "reqs,output",
+    [
+        [
+            """
+            spacy
+
+            # comment
+
+            thinc""",
+            (False, False),
+        ],
+        [
+            """# comment
+            --some-flag
+            spacy""",
+            (False, False),
+        ],
+        [
+            """# comment
+            --some-flag
+            spacy; python_version >= '3.6'""",
+            (False, False),
+        ],
+        [
+            """# comment
+             spacyunknowndoesnotexist12345""",
+            (True, False),
+        ],
+    ],
+)
+def test_project_check_requirements(reqs, output):
+    # excessive guard against unlikely package name
+    try:
+        pkg_resources.require("spacyunknowndoesnotexist12345")
+    except pkg_resources.DistributionNotFound:
+        assert output == _check_requirements([req.strip() for req in reqs.split("\n")])

spacy/tests/test_models.py

@@ -23,7 +23,7 @@ def get_textcat_bow_kwargs():
 
 
 def get_textcat_cnn_kwargs():
-    return {"tok2vec": test_tok2vec(), "exclusive_classes": False, "nO": 13}
+    return {"tok2vec": make_test_tok2vec(), "exclusive_classes": False, "nO": 13}
 
 
 def get_all_params(model):
@@ -65,7 +65,7 @@ def get_tok2vec_kwargs():
     }
 
 
-def test_tok2vec():
+def make_test_tok2vec():
     return build_Tok2Vec_model(**get_tok2vec_kwargs())
 
 

spacy/tests/test_scorer.py

@@ -474,3 +474,50 @@ def test_prf_score():
     assert (a.precision, a.recall, a.fscore) == approx(
         (c.precision, c.recall, c.fscore)
     )
+
+
+def test_score_cats(en_tokenizer):
+    text = "some text"
+    gold_doc = en_tokenizer(text)
+    gold_doc.cats = {"POSITIVE": 1.0, "NEGATIVE": 0.0}
+    pred_doc = en_tokenizer(text)
+    pred_doc.cats = {"POSITIVE": 0.75, "NEGATIVE": 0.25}
+    example = Example(pred_doc, gold_doc)
+    # threshold is ignored for multi_label=False
+    scores1 = Scorer.score_cats(
+        [example],
+        "cats",
+        labels=list(gold_doc.cats.keys()),
+        multi_label=False,
+        positive_label="POSITIVE",
+        threshold=0.1,
+    )
+    scores2 = Scorer.score_cats(
+        [example],
+        "cats",
+        labels=list(gold_doc.cats.keys()),
+        multi_label=False,
+        positive_label="POSITIVE",
+        threshold=0.9,
+    )
+    assert scores1["cats_score"] == 1.0
+    assert scores2["cats_score"] == 1.0
+    assert scores1 == scores2
+    # threshold is relevant for multi_label=True
+    scores = Scorer.score_cats(
+        [example],
+        "cats",
+        labels=list(gold_doc.cats.keys()),
+        multi_label=True,
+        threshold=0.9,
+    )
+    assert scores["cats_macro_f"] == 0.0
+    # threshold is relevant for multi_label=True
+    scores = Scorer.score_cats(
+        [example],
+        "cats",
+        labels=list(gold_doc.cats.keys()),
+        multi_label=True,
+        threshold=0.1,
+    )
+    assert scores["cats_macro_f"] == 0.5

spacy/tokens/_dict_proxies.py

@@ -42,7 +42,8 @@ class SpanGroups(UserDict):
     def copy(self, doc: Optional["Doc"] = None) -> "SpanGroups":
         if doc is None:
             doc = self._ensure_doc()
-        return SpanGroups(doc).from_bytes(self.to_bytes())
+        data_copy = ((k, v.copy(doc=doc)) for k, v in self.items())
+        return SpanGroups(doc, items=data_copy)
 
     def setdefault(self, key, default=None):
         if not isinstance(default, SpanGroup):

spacy/tokens/doc.pyx

@@ -1608,24 +1608,20 @@ cdef class Doc:
                 Doc.set_extension(attr)
             self._.set(attr, doc_json["_"][attr])
 
-        if doc_json.get("underscore_token", {}):
+        for token_attr in doc_json.get("underscore_token", {}):
-            for token_attr in doc_json["underscore_token"]:
-                token_start = doc_json["underscore_token"][token_attr]["token_start"]
-                value = doc_json["underscore_token"][token_attr]["value"]
-
             if not Token.has_extension(token_attr):
                 Token.set_extension(token_attr)
-                self[token_start]._.set(token_attr, value)
+            for token_data in doc_json["underscore_token"][token_attr]:
-                
+                start = token_by_char(self.c, self.length, token_data["start"])
-        if doc_json.get("underscore_span", {}):
+                value = token_data["value"]
-            for span_attr in doc_json["underscore_span"]:
+                self[start]._.set(token_attr, value)
-                token_start = doc_json["underscore_span"][span_attr]["token_start"]
-                token_end = doc_json["underscore_span"][span_attr]["token_end"]
-                value = doc_json["underscore_span"][span_attr]["value"]
                 
+        for span_attr in doc_json.get("underscore_span", {}):
             if not Span.has_extension(span_attr):
                 Span.set_extension(span_attr)
-                self[token_start:token_end]._.set(span_attr, value)
+            for span_data in doc_json["underscore_span"][span_attr]:
+                value = span_data["value"]
+                self.char_span(span_data["start"], span_data["end"])._.set(span_attr, value)
         return self
 
     def to_json(self, underscore=None):
@@ -1672,31 +1668,44 @@ cdef class Doc:
 
         if underscore:
             user_keys = set()
-            if self.user_data:
+            # Handle doc attributes with .get to include values from getters
+            # and not only values stored in user_data, for backwards
+            # compatibility
+            for attr in underscore:
+                if self.has_extension(attr):
+                    if "_" not in data:
                         data["_"] = {}
-                data["underscore_token"] = {}
+                    value = self._.get(attr)
-                data["underscore_span"] = {}
+                    if not srsly.is_json_serializable(value):
-                for data_key in self.user_data:
+                        raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
+                    data["_"][attr] = value
+                    user_keys.add(attr)
+            # Token and span attributes only include values stored in user_data
+            # and not values generated by getters
+            if self.user_data:
+                for data_key, value in self.user_data.copy().items():
                     if type(data_key) == tuple and len(data_key) >= 4 and data_key[0] == "._.":
                         attr = data_key[1]
                         start = data_key[2]
                         end = data_key[3]
                         if attr in underscore:
                             user_keys.add(attr)
-                            value = self.user_data[data_key]
                             if not srsly.is_json_serializable(value):
                                 raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
-                            # Check if doc attribute
+                            # Token attribute
-                            if start is None:
+                            if start is not None and end is None:
-                                data["_"][attr] = value
+                                if "underscore_token" not in data:
-                            # Check if token attribute
+                                    data["underscore_token"] = {}
-                            elif end is None:
                                 if attr not in data["underscore_token"]:
-                                    data["underscore_token"][attr] = {"token_start": start, "value": value}
+                                    data["underscore_token"][attr] = []
-                            # Else span attribute
+                                data["underscore_token"][attr].append({"start": start, "value": value})
-                            else:
+                            # Span attribute
+                            elif start is not None and end is not None:
+                                if "underscore_span" not in data:
+                                    data["underscore_span"] = {}
                                 if attr not in data["underscore_span"]:
-                                    data["underscore_span"][attr] = {"token_start": start, "token_end": end, "value": value}
+                                    data["underscore_span"][attr] = []
+                                data["underscore_span"][attr].append({"start": start, "end": end, "value": value})
 
             for attr in underscore:
                 if attr not in user_keys:

spacy/tokens/span.pyi

@@ -117,15 +117,13 @@ class Span:
     end_char: int
     label: int
     kb_id: int
+    id: int
     ent_id: int
     ent_id_: str
     @property
-    def id(self) -> int: ...
-    @property
-    def id_(self) -> str: ...
-    @property
     def orth_(self) -> str: ...
     @property
     def lemma_(self) -> str: ...
     label_: str
     kb_id_: str
+    id_: str

spacy/tokens/span_group.pyi

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable
+from typing import Any, Dict, Iterable, Optional
 from .doc import Doc
 from .span import Span
 
@@ -24,4 +24,4 @@ class SpanGroup:
     def __getitem__(self, i: int) -> Span: ...
     def to_bytes(self) -> bytes: ...
     def from_bytes(self, bytes_data: bytes) -> SpanGroup: ...
-    def copy(self) -> SpanGroup: ...
+    def copy(self, doc: Optional[Doc] = ...) -> SpanGroup: ...

spacy/tokens/span_group.pyx

@@ -241,15 +241,18 @@ cdef class SpanGroup:
     cdef void push_back(self, SpanC span) nogil:
         self.c.push_back(span)
 
-    def copy(self)  -> SpanGroup:
+    def copy(self, doc: Optional["Doc"] = None) -> SpanGroup:
         """Clones the span group.
 
+        doc (Doc): New reference document to which the copy is bound.
         RETURNS (SpanGroup): A copy of the span group.
 
         DOCS: https://spacy.io/api/spangroup#copy
         """
+        if doc is None:
+            doc = self.doc
         return SpanGroup(
-            self.doc,
+            doc,
             name=self.name,
             attrs=deepcopy(self.attrs),
             spans=list(self),

spacy/util.py

@@ -443,9 +443,9 @@ def load_model_from_package(
     name: str,
     *,
     vocab: Union["Vocab", bool] = True,
-    disable: Union[str, Iterable[str]] = SimpleFrozenList(),
+    disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
-    enable: Union[str, Iterable[str]] = SimpleFrozenList(),
+    enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
-    exclude: Union[str, Iterable[str]] = SimpleFrozenList(),
+    exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
 ) -> "Language":
     """Load a model from an installed package.
@@ -619,9 +619,9 @@ def load_model_from_init_py(
     init_file: Union[Path, str],
     *,
     vocab: Union["Vocab", bool] = True,
-    disable: Union[str, Iterable[str]] = SimpleFrozenList(),
+    disable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
-    enable: Union[str, Iterable[str]] = SimpleFrozenList(),
+    enable: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
-    exclude: Union[str, Iterable[str]] = SimpleFrozenList(),
+    exclude: Union[str, Iterable[str]] = _DEFAULT_EMPTY_PIPES,
     config: Union[Dict[str, Any], Config] = SimpleFrozenDict(),
 ) -> "Language":
     """Helper function to use in the `load()` method of a model package's

website/README.md

@@ -155,7 +155,7 @@ import Tag from 'components/tag'
 
 > ```jsx
 > <Tag>method</Tag>
-> <Tag variant="new">2.1</Tag>
+> <Tag variant="new">4</Tag>
 > <Tag variant="model">tagger, parser</Tag>
 > ```
 
@@ -170,7 +170,7 @@ installed.
 
 <InlineList>
 
-<Tag>method</Tag> <Tag variant="new">2</Tag> <Tag variant="model">tagger,
+<Tag>method</Tag> <Tag variant="new">4</Tag> <Tag variant="model">tagger,
 parser</Tag>
 
 </InlineList>

website/docs/api/cli.md

@@ -53,7 +53,7 @@ $ python -m spacy download [model] [--direct] [--sdist] [pip_args]
 | `--direct`, `-D`                           | Force direct download of exact package version. ~~bool (flag)~~                                                                                                                                                                           |
 | `--sdist`, `-S` <Tag variant="new">3</Tag> | Download the source package (`.tar.gz` archive) instead of the default pre-built binary wheel. ~~bool (flag)~~                                                                                                                            |
 | `--help`, `-h`                             | Show help message and available arguments. ~~bool (flag)~~                                                                                                                                                                                |
-| pip args <Tag variant="new">2.1</Tag>      | Additional installation options to be passed to `pip install` when installing the pipeline package. For example, `--user` to install to the user home directory or `--no-deps` to not install package dependencies. ~~Any (option/flag)~~ |
+| pip args                                   | Additional installation options to be passed to `pip install` when installing the pipeline package. For example, `--user` to install to the user home directory or `--no-deps` to not install package dependencies. ~~Any (option/flag)~~ |
 | **CREATES**                                | The installed pipeline package in your `site-packages` directory.                                                                                                                                                                         |
 
 ## info {#info tag="command"}
@@ -78,10 +78,10 @@ $ python -m spacy info [model] [--markdown] [--silent] [--exclude]
 ```
 
 | Name                                         | Description                                                                                                             |
-| ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------- |
+| -------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
 | `model`                                      | A trained pipeline, i.e. package name or path (optional). ~~Optional[str] \(option)~~                                   |
 | `--markdown`, `-md`                          | Print information as Markdown. ~~bool (flag)~~                                                                          |
-| `--silent`, `-s` <Tag variant="new">2.0.12</Tag> | Don't print anything, just return the values. ~~bool (flag)~~                                                           |
+| `--silent`, `-s`                             | Don't print anything, just return the values. ~~bool (flag)~~                                                           |
 | `--exclude`, `-e`                            | Comma-separated keys to exclude from the print-out. Defaults to `"labels"`. ~~Optional[str]~~                           |
 | `--url`, `-u` <Tag variant="new">3.5.0</Tag> | Print the URL to download the most recent compatible version of the pipeline. Requires a pipeline name. ~~bool (flag)~~ |
 | `--help`, `-h`                               | Show help message and available arguments. ~~bool (flag)~~                                                              |
@@ -261,18 +261,18 @@ $ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type]
 ```
 
 | Name                      | Description                                                                                                                               |
-| ------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
 | `input_path`              | Input file or directory. ~~Path (positional)~~                                                                                            |
 | `output_dir`              | Output directory for converted file. Defaults to `"-"`, meaning data will be written to `stdout`. ~~Optional[Path] \(option)~~            |
-| `--converter`, `-c` <Tag variant="new">2</Tag>   | Name of converter to use (see below). ~~str (option)~~                                                                                    |
+| `--converter`, `-c`       | Name of converter to use (see below). ~~str (option)~~                                                                                    |
-| `--file-type`, `-t` <Tag variant="new">2.1</Tag> | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ |
+| `--file-type`, `-t`       | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ |
 | `--n-sents`, `-n`         | Number of sentences per document. Supported for: `conll`, `conllu`, `iob`, `ner` ~~int (option)~~                                         |
-| `--seg-sents`, `-s` <Tag variant="new">2.2</Tag> | Segment sentences. Supported for: `conll`, `ner` ~~bool (flag)~~                                                                          |
+| `--seg-sents`, `-s`       | Segment sentences. Supported for: `conll`, `ner` ~~bool (flag)~~                                                                          |
 | `--base`, `-b`, `--model` | Trained spaCy pipeline for sentence segmentation to use as base (for `--seg-sents`). ~~Optional[str](option)~~                            |
 | `--morphology`, `-m`      | Enable appending morphology to tags. Supported for: `conllu` ~~bool (flag)~~                                                              |
 | `--merge-subtokens`, `-T` | Merge CoNLL-U subtokens ~~bool (flag)~~                                                                                                   |
 | `--ner-map`, `-nm`        | NER tag mapping (as JSON-encoded dict of entity types). Supported for: `conllu` ~~Optional[Path](option)~~                                |
-| `--lang`, `-l` <Tag variant="new">2.1</Tag>      | Language code (if tokenizer required). ~~Optional[str] \(option)~~                                                                        |
+| `--lang`, `-l`            | Language code (if tokenizer required). ~~Optional[str] \(option)~~                                                                        |
 | `--concatenate`, `-C`     | Concatenate output to a single file ~~bool (flag)~~                                                                                       |
 | `--help`, `-h`            | Show help message and available arguments. ~~bool (flag)~~                                                                                |
 | **CREATES**               | Binary [`DocBin`](/api/docbin) training data that can be used with [`spacy train`](/api/cli#train).                                       |
@@ -474,8 +474,7 @@ report span characteristics such as the average span length and the span (or
 span boundary) distinctiveness. The distinctiveness measure shows how different
 the tokens are with respect to the rest of the corpus using the KL-divergence of
 the token distributions. To learn more, you can check out Papay et al.'s work on
-[*Dissecting Span Identification Tasks with Performance Prediction* (EMNLP
+[*Dissecting Span Identification Tasks with Performance Prediction* (EMNLP 2020)](https://aclanthology.org/2020.emnlp-main.396/).
-2020)](https://aclanthology.org/2020.emnlp-main.396/).
 
 </Infobox>
 
@@ -1230,12 +1229,12 @@ $ python -m spacy package [input_dir] [output_dir] [--code] [--meta-path] [--cre
 > ```
 
 | Name                                         | Description                                                                                                                                                                                                                                                                      |
-| ------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `input_dir`                                  | Path to directory containing pipeline data. ~~Path (positional)~~                                                                                                                                                                                                                |
 | `output_dir`                                 | Directory to create package folder in. ~~Path (positional)~~                                                                                                                                                                                                                     |
 | `--code`, `-c` <Tag variant="new">3</Tag>    | Comma-separated paths to Python files to be included in the package and imported in its `__init__.py`. This allows including [registering functions](/usage/training#custom-functions) and [custom components](/usage/processing-pipelines#custom-components). ~~str (option)~~  |
-| `--meta-path`, `-m` <Tag variant="new">2</Tag>   | Path to [`meta.json`](/api/data-formats#meta) file (optional). ~~Optional[Path] \(option)~~                                                                                                                                                                                      |
+| `--meta-path`, `-m`                          | Path to [`meta.json`](/api/data-formats#meta) file (optional). ~~Optional[Path] \(option)~~                                                                                                                                                                                      |
-| `--create-meta`, `-C` <Tag variant="new">2</Tag> | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. ~~bool (flag)~~                                                                  |
+| `--create-meta`, `-C`                        | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. ~~bool (flag)~~                                                                  |
 | `--build`, `-b` <Tag variant="new">3</Tag>   | Comma-separated artifact formats to build. Can be `sdist` (for a `.tar.gz` archive) and/or `wheel` (for a binary `.whl` file), or `none` if you want to run this step manually. The generated artifacts can be installed by `pip install`. Defaults to `sdist`. ~~str (option)~~ |
 | `--name`, `-n` <Tag variant="new">3</Tag>    | Package name to override in meta. ~~Optional[str] \(option)~~                                                                                                                                                                                                                    |
 | `--version`, `-v` <Tag variant="new">3</Tag> | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. ~~Optional[str] \(option)~~                                                                                                                             |

website/docs/api/doc.md

@@ -210,11 +210,11 @@ alignment mode `"strict".
 > ```
 
 | Name             | Description                                                                                                                                                                                                                                                                  |
-| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `start`          | The index of the first character of the span. ~~int~~                                                                                                                                                                                                                        |
 | `end`            | The index of the last character after the span. ~~int~~                                                                                                                                                                                                                      |
 | `label`          | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~                                                                                                                                                                                                  |
-| `kb_id` <Tag variant="new">2.2</Tag> | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~                                                                                                                                                                                    |
+| `kb_id`          | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~                                                                                                                                                                                    |
 | `vector`         | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~                                                                                                                                                                                               |
 | `alignment_mode` | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ |
 | **RETURNS**      | The newly constructed object or `None`. ~~Optional[Span]~~                                                                                                                                                                                                                   |
@@ -752,15 +752,15 @@ The L2 norm of the document's vector representation.
 ## Attributes {#attributes}
 
 | Name                 | Description                                                                                                                         |
-| ------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------- |
+| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
 | `text`               | A string representation of the document text. ~~str~~                                                                               |
 | `text_with_ws`       | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. ~~str~~                                       |
 | `mem`                | The document's local memory heap, for all C data it owns. ~~cymem.Pool~~                                                            |
 | `vocab`              | The store of lexical types. ~~Vocab~~                                                                                               |
-| `tensor` <Tag variant="new">2</Tag>  | Container for dense vector representations. ~~numpy.ndarray~~                                                                       |
+| `tensor`             | Container for dense vector representations. ~~numpy.ndarray~~                                                                       |
 | `user_data`          | A generic storage area, for user custom data. ~~Dict[str, Any]~~                                                                    |
-| `lang` <Tag variant="new">2.1</Tag>  | Language of the document's vocabulary. ~~int~~                                                                                      |
+| `lang`               | Language of the document's vocabulary. ~~int~~                                                                                      |
-| `lang_` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~str~~                                                                                      |
+| `lang_`              | Language of the document's vocabulary. ~~str~~                                                                                      |
 | `sentiment`          | The document's positivity/negativity score, if available. ~~float~~                                                                 |
 | `user_hooks`         | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~                                           |
 | `user_token_hooks`   | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~                                   |

website/docs/api/entitylinker.md

@@ -14,7 +14,8 @@ entities) to unique identifiers, grounding the named entities into the "real
 world". It requires a `KnowledgeBase`, as well as a function to generate
 plausible candidates from that `KnowledgeBase` given a certain textual mention,
 and a machine learning model to pick the right candidate, given the local
-context of the mention.
+context of the mention. `EntityLinker` defaults to using the
+[`InMemoryLookupKB`](/api/kb_in_memory) implementation.
 
 ## Assigned Attributes {#assigned-attributes}
 
@@ -170,7 +171,7 @@ with the current vocab.
 >
 > ```python
 > def create_kb(vocab):
->     kb = KnowledgeBase(vocab, entity_vector_length=128)
+>     kb = InMemoryLookupKB(vocab, entity_vector_length=128)
 >     kb.add_entity(...)
 >     kb.add_alias(...)
 >     return kb

website/docs/api/kb.md

@@ -4,27 +4,45 @@ teaser:
   A storage class for entities and aliases of a specific knowledge base
   (ontology)
 tag: class
-source: spacy/kb.pyx
+source: spacy/kb/kb.pyx
 new: 2.2
 ---
 
-The `KnowledgeBase` object provides a method to generate
+The `KnowledgeBase` object is an abstract class providing a method to generate
-[`Candidate`](/api/kb/#candidate) objects, which are plausible external
+[`Candidate`](/api/kb#candidate) objects, which are plausible external
 identifiers given a certain textual mention. Each such `Candidate` holds
 information from the relevant KB entities, such as its frequency in text and
 possible aliases. Each entity in the knowledge base also has a pretrained entity
 vector of a fixed size.
 
+Beyond that, `KnowledgeBase` classes have to implement a number of utility
+functions called by the [`EntityLinker`](/api/entitylinker) component.
+
+<Infobox variant="warning">
+
+This class was not abstract up to spaCy version 3.5. The `KnowledgeBase`
+implementation up to that point is available as `InMemoryLookupKB` from 3.5
+onwards.
+
+</Infobox>
+
 ## KnowledgeBase.\_\_init\_\_ {#init tag="method"}
 
-Create the knowledge base.
+`KnowledgeBase` is an abstract class and cannot be instantiated. Its child
+classes should call `__init__()` to set up some necessary attributes.
 
 > #### Example
 >
 > ```python
 > from spacy.kb import KnowledgeBase
+> from spacy.vocab import Vocab
+>
+> class FullyImplementedKB(KnowledgeBase):
+>   def __init__(self, vocab: Vocab, entity_vector_length: int):
+>       super().__init__(vocab, entity_vector_length)
+>       ...
 > vocab = nlp.vocab
-> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
+> kb = FullyImplementedKB(vocab=vocab, entity_vector_length=64)
 > ```
 
 | Name                   | Description                                      |
@@ -40,133 +58,66 @@ The length of the fixed-size entity vectors in the knowledge base.
 | ----------- | ------------------------------------------------ |
 | **RETURNS** | Length of the fixed-size entity vectors. ~~int~~ |
 
-## KnowledgeBase.add_entity {#add_entity tag="method"}
+## KnowledgeBase.get_candidates {#get_candidates tag="method"}
 
-Add an entity to the knowledge base, specifying its corpus frequency and entity
+Given a certain textual mention as input, retrieve a list of candidate entities
-vector, which should be of length
+of type [`Candidate`](/api/kb#candidate).
-[`entity_vector_length`](/api/kb#entity_vector_length).
 
 > #### Example
 >
 > ```python
-> kb.add_entity(entity="Q42", freq=32, entity_vector=vector1)
+> from spacy.lang.en import English
-> kb.add_entity(entity="Q463035", freq=111, entity_vector=vector2)
+> nlp = English()
+> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
+> candidates = kb.get_candidates(doc[0:2])
 > ```
 
 | Name        | Description                                                          |
-| --------------- | ---------------------------------------------------------- |
+| ----------- | -------------------------------------------------------------------- |
-| `entity`        | The unique entity identifier. ~~str~~                      |
+| `mention`   | The textual mention or alias. ~~Span~~                               |
-| `freq`          | The frequency of the entity in a typical corpus. ~~float~~ |
+| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[Candidate]~~ |
-| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~     |
 
-## KnowledgeBase.set_entities {#set_entities tag="method"}
+## KnowledgeBase.get_candidates_batch {#get_candidates_batch tag="method"}
 
-Define the full list of entities in the knowledge base, specifying the corpus
+Same as [`get_candidates()`](/api/kb#get_candidates), but for an arbitrary
-frequency and entity vector for each entity.
+number of mentions. The [`EntityLinker`](/api/entitylinker) component will call
+`get_candidates_batch()` instead of `get_candidates()`, if the config parameter
+`candidates_batch_size` is greater or equal than 1.
+
+The default implementation of `get_candidates_batch()` executes
+`get_candidates()` in a loop. We recommend implementing a more efficient way to
+retrieve candidates for multiple mentions at once, if performance is of concern
+to you.
 
 > #### Example
 >
 > ```python
-> kb.set_entities(entity_list=["Q42", "Q463035"], freq_list=[32, 111], vector_list=[vector1, vector2])
+> from spacy.lang.en import English
+> nlp = English()
+> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
+> candidates = kb.get_candidates((doc[0:2], doc[3:]))
 > ```
 
 | Name        | Description                                                                                  |
-| ------------- | ---------------------------------------------------------------- |
+| ----------- | -------------------------------------------------------------------------------------------- |
-| `entity_list` | List of unique entity identifiers. ~~Iterable[Union[str, int]]~~ |
+| `mentions`  | The textual mention or alias. ~~Iterable[Span]~~                                             |
-| `freq_list`   | List of entity frequencies. ~~Iterable[int]~~                    |
+| **RETURNS** | An iterable of iterable with relevant `Candidate` objects. ~~Iterable[Iterable[Candidate]]~~ |
-| `vector_list` | List of entity vectors. ~~Iterable[numpy.ndarray]~~              |
-
-## KnowledgeBase.add_alias {#add_alias tag="method"}
-
-Add an alias or mention to the knowledge base, specifying its potential KB
-identifiers and their prior probabilities. The entity identifiers should refer
-to entities previously added with [`add_entity`](/api/kb#add_entity) or
-[`set_entities`](/api/kb#set_entities). The sum of the prior probabilities
-should not exceed 1. Note that an empty string can not be used as alias.
-
-> #### Example
->
-> ```python
-> kb.add_alias(alias="Douglas", entities=["Q42", "Q463035"], probabilities=[0.6, 0.3])
-> ```
-
-| Name            | Description                                                                       |
-| --------------- | --------------------------------------------------------------------------------- |
-| `alias`         | The textual mention or alias. Can not be the empty string. ~~str~~                |
-| `entities`      | The potential entities that the alias may refer to. ~~Iterable[Union[str, int]]~~ |
-| `probabilities` | The prior probabilities of each entity. ~~Iterable[float]~~                       |
-
-## KnowledgeBase.\_\_len\_\_ {#len tag="method"}
-
-Get the total number of entities in the knowledge base.
-
-> #### Example
->
-> ```python
-> total_entities = len(kb)
-> ```
-
-| Name        | Description                                           |
-| ----------- | ----------------------------------------------------- |
-| **RETURNS** | The number of entities in the knowledge base. ~~int~~ |
-
-## KnowledgeBase.get_entity_strings {#get_entity_strings tag="method"}
-
-Get a list of all entity IDs in the knowledge base.
-
-> #### Example
->
-> ```python
-> all_entities = kb.get_entity_strings()
-> ```
-
-| Name        | Description                                               |
-| ----------- | --------------------------------------------------------- |
-| **RETURNS** | The list of entities in the knowledge base. ~~List[str]~~ |
-
-## KnowledgeBase.get_size_aliases {#get_size_aliases tag="method"}
-
-Get the total number of aliases in the knowledge base.
-
-> #### Example
->
-> ```python
-> total_aliases = kb.get_size_aliases()
-> ```
-
-| Name        | Description                                          |
-| ----------- | ---------------------------------------------------- |
-| **RETURNS** | The number of aliases in the knowledge base. ~~int~~ |
-
-## KnowledgeBase.get_alias_strings {#get_alias_strings tag="method"}
-
-Get a list of all aliases in the knowledge base.
-
-> #### Example
->
-> ```python
-> all_aliases = kb.get_alias_strings()
-> ```
-
-| Name        | Description                                              |
-| ----------- | -------------------------------------------------------- |
-| **RETURNS** | The list of aliases in the knowledge base. ~~List[str]~~ |
 
 ## KnowledgeBase.get_alias_candidates {#get_alias_candidates tag="method"}
 
-Given a certain textual mention as input, retrieve a list of candidate entities
+<Infobox variant="warning">
-of type [`Candidate`](/api/kb/#candidate).
+This method is _not_ available from spaCy 3.5 onwards.
+</Infobox>
 
-> #### Example
+From spaCy 3.5 on `KnowledgeBase` is an abstract class (with
->
+[`InMemoryLookupKB`](/api/kb_in_memory) being a drop-in replacement) to allow
-> ```python
+more flexibility in customizing knowledge bases. Some of its methods were moved
-> candidates = kb.get_alias_candidates("Douglas")
+to [`InMemoryLookupKB`](/api/kb_in_memory) during this refactoring, one of those
-> ```
+being `get_alias_candidates()`. This method is now available as
-
+[`InMemoryLookupKB.get_alias_candidates()`](/api/kb_in_memory#get_alias_candidates).
-| Name        | Description                                                   |
+Note: [`InMemoryLookupKB.get_candidates()`](/api/kb_in_memory#get_candidates)
-| ----------- | ------------------------------------------------------------- |
+defaults to
-| `alias`     | The textual mention or alias. ~~str~~                         |
+[`InMemoryLookupKB.get_alias_candidates()`](/api/kb_in_memory#get_alias_candidates).
-| **RETURNS** | The list of relevant `Candidate` objects. ~~List[Candidate]~~ |
 
 ## KnowledgeBase.get_vector {#get_vector tag="method"}
 
@@ -179,26 +130,29 @@ Given a certain entity ID, retrieve its pretrained entity vector.
 > ```
 
 | Name        | Description                            |
-| ----------- | ------------------------------------ |
+| ----------- | -------------------------------------- |
 | `entity`    | The entity ID. ~~str~~                 |
-| **RETURNS** | The entity vector. ~~numpy.ndarray~~ |
+| **RETURNS** | The entity vector. ~~Iterable[float]~~ |
 
-## KnowledgeBase.get_prior_prob {#get_prior_prob tag="method"}
+## KnowledgeBase.get_vectors {#get_vectors tag="method"}
 
-Given a certain entity ID and a certain textual mention, retrieve the prior
+Same as [`get_vector()`](/api/kb#get_vector), but for an arbitrary number of
-probability of the fact that the mention links to the entity ID.
+entity IDs.
+
+The default implementation of `get_vectors()` executes `get_vector()` in a loop.
+We recommend implementing a more efficient way to retrieve vectors for multiple
+entities at once, if performance is of concern to you.
 
 > #### Example
 >
 > ```python
-> probability = kb.get_prior_prob("Q42", "Douglas")
+> vectors = kb.get_vectors(("Q42", "Q3107329"))
 > ```
 
 | Name        | Description                                               |
-| ----------- | ------------------------------------------------------------------------- |
+| ----------- | --------------------------------------------------------- |
-| `entity`    | The entity ID. ~~str~~                                                    |
+| `entities`  | The entity IDs. ~~Iterable[str]~~                         |
-| `alias`     | The textual mention or alias. ~~str~~                                     |
+| **RETURNS** | The entity vectors. ~~Iterable[Iterable[numpy.ndarray]]~~ |
-| **RETURNS** | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
 
 ## KnowledgeBase.to_disk {#to_disk tag="method"}
 
@@ -207,12 +161,13 @@ Save the current state of the knowledge base to a directory.
 > #### Example
 >
 > ```python
-> kb.to_disk(loc)
+> kb.to_disk(path)
 > ```
 
 | Name      | Description                                                                                                                                |
-| ----- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
-| `loc` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
+| `path`    | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
+| `exclude` | List of components to exclude. ~~Iterable[str]~~                                                                                           |
 
 ## KnowledgeBase.from_disk {#from_disk tag="method"}
 
@@ -222,16 +177,16 @@ Restore the state of the knowledge base from a given directory. Note that the
 > #### Example
 >
 > ```python
-> from spacy.kb import KnowledgeBase
 > from spacy.vocab import Vocab
 > vocab = Vocab().from_disk("/path/to/vocab")
-> kb = KnowledgeBase(vocab=vocab, entity_vector_length=64)
+> kb = FullyImplementedKB(vocab=vocab, entity_vector_length=64)
 > kb.from_disk("/path/to/kb")
 > ```
 
 | Name        | Description                                                                                     |
 | ----------- | ----------------------------------------------------------------------------------------------- |
 | `loc`       | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
+| `exclude`   | List of components to exclude. ~~Iterable[str]~~                                                |
 | **RETURNS** | The modified `KnowledgeBase` object. ~~KnowledgeBase~~                                          |
 
 ## Candidate {#candidate tag="class"}

website/docs/api/kb_in_memory.md

@@ -0,0 +1,302 @@
+---
+title: InMemoryLookupKB
+teaser:
+  The default implementation of the KnowledgeBase interface. Stores all
+  information in-memory.
+tag: class
+source: spacy/kb/kb_in_memory.pyx
+new: 3.5
+---
+
+The `InMemoryLookupKB` class inherits from [`KnowledgeBase`](/api/kb) and
+implements all of its methods. It stores all KB data in-memory and generates
+[`Candidate`](/api/kb#candidate) objects by exactly matching mentions with
+entity names. It's highly optimized for both a low memory footprint and speed of
+retrieval.
+
+## InMemoryLookupKB.\_\_init\_\_ {#init tag="method"}
+
+Create the knowledge base.
+
+> #### Example
+>
+> ```python
+> from spacy.kb import InMemoryLookupKB
+> vocab = nlp.vocab
+> kb = InMemoryLookupKB(vocab=vocab, entity_vector_length=64)
+> ```
+
+| Name                   | Description                                      |
+| ---------------------- | ------------------------------------------------ |
+| `vocab`                | The shared vocabulary. ~~Vocab~~                 |
+| `entity_vector_length` | Length of the fixed-size entity vectors. ~~int~~ |
+
+## InMemoryLookupKB.entity_vector_length {#entity_vector_length tag="property"}
+
+The length of the fixed-size entity vectors in the knowledge base.
+
+| Name        | Description                                      |
+| ----------- | ------------------------------------------------ |
+| **RETURNS** | Length of the fixed-size entity vectors. ~~int~~ |
+
+## InMemoryLookupKB.add_entity {#add_entity tag="method"}
+
+Add an entity to the knowledge base, specifying its corpus frequency and entity
+vector, which should be of length
+[`entity_vector_length`](/api/kb_in_memory#entity_vector_length).
+
+> #### Example
+>
+> ```python
+> kb.add_entity(entity="Q42", freq=32, entity_vector=vector1)
+> kb.add_entity(entity="Q463035", freq=111, entity_vector=vector2)
+> ```
+
+| Name            | Description                                                |
+| --------------- | ---------------------------------------------------------- |
+| `entity`        | The unique entity identifier. ~~str~~                      |
+| `freq`          | The frequency of the entity in a typical corpus. ~~float~~ |
+| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~     |
+
+## InMemoryLookupKB.set_entities {#set_entities tag="method"}
+
+Define the full list of entities in the knowledge base, specifying the corpus
+frequency and entity vector for each entity.
+
+> #### Example
+>
+> ```python
+> kb.set_entities(entity_list=["Q42", "Q463035"], freq_list=[32, 111], vector_list=[vector1, vector2])
+> ```
+
+| Name          | Description                                                      |
+| ------------- | ---------------------------------------------------------------- |
+| `entity_list` | List of unique entity identifiers. ~~Iterable[Union[str, int]]~~ |
+| `freq_list`   | List of entity frequencies. ~~Iterable[int]~~                    |
+| `vector_list` | List of entity vectors. ~~Iterable[numpy.ndarray]~~              |
+
+## InMemoryLookupKB.add_alias {#add_alias tag="method"}
+
+Add an alias or mention to the knowledge base, specifying its potential KB
+identifiers and their prior probabilities. The entity identifiers should refer
+to entities previously added with [`add_entity`](/api/kb_in_memory#add_entity)
+or [`set_entities`](/api/kb_in_memory#set_entities). The sum of the prior
+probabilities should not exceed 1. Note that an empty string can not be used as
+alias.
+
+> #### Example
+>
+> ```python
+> kb.add_alias(alias="Douglas", entities=["Q42", "Q463035"], probabilities=[0.6, 0.3])
+> ```
+
+| Name            | Description                                                                       |
+| --------------- | --------------------------------------------------------------------------------- |
+| `alias`         | The textual mention or alias. Can not be the empty string. ~~str~~                |
+| `entities`      | The potential entities that the alias may refer to. ~~Iterable[Union[str, int]]~~ |
+| `probabilities` | The prior probabilities of each entity. ~~Iterable[float]~~                       |
+
+## InMemoryLookupKB.\_\_len\_\_ {#len tag="method"}
+
+Get the total number of entities in the knowledge base.
+
+> #### Example
+>
+> ```python
+> total_entities = len(kb)
+> ```
+
+| Name        | Description                                           |
+| ----------- | ----------------------------------------------------- |
+| **RETURNS** | The number of entities in the knowledge base. ~~int~~ |
+
+## InMemoryLookupKB.get_entity_strings {#get_entity_strings tag="method"}
+
+Get a list of all entity IDs in the knowledge base.
+
+> #### Example
+>
+> ```python
+> all_entities = kb.get_entity_strings()
+> ```
+
+| Name        | Description                                               |
+| ----------- | --------------------------------------------------------- |
+| **RETURNS** | The list of entities in the knowledge base. ~~List[str]~~ |
+
+## InMemoryLookupKB.get_size_aliases {#get_size_aliases tag="method"}
+
+Get the total number of aliases in the knowledge base.
+
+> #### Example
+>
+> ```python
+> total_aliases = kb.get_size_aliases()
+> ```
+
+| Name        | Description                                          |
+| ----------- | ---------------------------------------------------- |
+| **RETURNS** | The number of aliases in the knowledge base. ~~int~~ |
+
+## InMemoryLookupKB.get_alias_strings {#get_alias_strings tag="method"}
+
+Get a list of all aliases in the knowledge base.
+
+> #### Example
+>
+> ```python
+> all_aliases = kb.get_alias_strings()
+> ```
+
+| Name        | Description                                              |
+| ----------- | -------------------------------------------------------- |
+| **RETURNS** | The list of aliases in the knowledge base. ~~List[str]~~ |
+
+## InMemoryLookupKB.get_candidates {#get_candidates tag="method"}
+
+Given a certain textual mention as input, retrieve a list of candidate entities
+of type [`Candidate`](/api/kb#candidate). Wraps
+[`get_alias_candidates()`](/api/kb_in_memory#get_alias_candidates).
+
+> #### Example
+>
+> ```python
+> from spacy.lang.en import English
+> nlp = English()
+> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
+> candidates = kb.get_candidates(doc[0:2])
+> ```
+
+| Name        | Description                                                          |
+| ----------- | -------------------------------------------------------------------- |
+| `mention`   | The textual mention or alias. ~~Span~~                               |
+| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[Candidate]~~ |
+
+## InMemoryLookupKB.get_candidates_batch {#get_candidates_batch tag="method"}
+
+Same as [`get_candidates()`](/api/kb_in_memory#get_candidates), but for an
+arbitrary number of mentions. The [`EntityLinker`](/api/entitylinker) component
+will call `get_candidates_batch()` instead of `get_candidates()`, if the config
+parameter `candidates_batch_size` is greater or equal than 1.
+
+The default implementation of `get_candidates_batch()` executes
+`get_candidates()` in a loop. We recommend implementing a more efficient way to
+retrieve candidates for multiple mentions at once, if performance is of concern
+to you.
+
+> #### Example
+>
+> ```python
+> from spacy.lang.en import English
+> nlp = English()
+> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
+> candidates = kb.get_candidates((doc[0:2], doc[3:]))
+> ```
+
+| Name        | Description                                                                                  |
+| ----------- | -------------------------------------------------------------------------------------------- |
+| `mentions`  | The textual mention or alias. ~~Iterable[Span]~~                                             |
+| **RETURNS** | An iterable of iterable with relevant `Candidate` objects. ~~Iterable[Iterable[Candidate]]~~ |
+
+## InMemoryLookupKB.get_alias_candidates {#get_alias_candidates tag="method"}
+
+Given a certain textual mention as input, retrieve a list of candidate entities
+of type [`Candidate`](/api/kb#candidate).
+
+> #### Example
+>
+> ```python
+> candidates = kb.get_alias_candidates("Douglas")
+> ```
+
+| Name        | Description                                                   |
+| ----------- | ------------------------------------------------------------- |
+| `alias`     | The textual mention or alias. ~~str~~                         |
+| **RETURNS** | The list of relevant `Candidate` objects. ~~List[Candidate]~~ |
+
+## InMemoryLookupKB.get_vector {#get_vector tag="method"}
+
+Given a certain entity ID, retrieve its pretrained entity vector.
+
+> #### Example
+>
+> ```python
+> vector = kb.get_vector("Q42")
+> ```
+
+| Name        | Description                          |
+| ----------- | ------------------------------------ |
+| `entity`    | The entity ID. ~~str~~               |
+| **RETURNS** | The entity vector. ~~numpy.ndarray~~ |
+
+## InMemoryLookupKB.get_vectors {#get_vectors tag="method"}
+
+Same as [`get_vector()`](/api/kb_in_memory#get_vector), but for an arbitrary
+number of entity IDs.
+
+The default implementation of `get_vectors()` executes `get_vector()` in a loop.
+We recommend implementing a more efficient way to retrieve vectors for multiple
+entities at once, if performance is of concern to you.
+
+> #### Example
+>
+> ```python
+> vectors = kb.get_vectors(("Q42", "Q3107329"))
+> ```
+
+| Name        | Description                                               |
+| ----------- | --------------------------------------------------------- |
+| `entities`  | The entity IDs. ~~Iterable[str]~~                         |
+| **RETURNS** | The entity vectors. ~~Iterable[Iterable[numpy.ndarray]]~~ |
+
+## InMemoryLookupKB.get_prior_prob {#get_prior_prob tag="method"}
+
+Given a certain entity ID and a certain textual mention, retrieve the prior
+probability of the fact that the mention links to the entity ID.
+
+> #### Example
+>
+> ```python
+> probability = kb.get_prior_prob("Q42", "Douglas")
+> ```
+
+| Name        | Description                                                               |
+| ----------- | ------------------------------------------------------------------------- |
+| `entity`    | The entity ID. ~~str~~                                                    |
+| `alias`     | The textual mention or alias. ~~str~~                                     |
+| **RETURNS** | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
+
+## InMemoryLookupKB.to_disk {#to_disk tag="method"}
+
+Save the current state of the knowledge base to a directory.
+
+> #### Example
+>
+> ```python
+> kb.to_disk(path)
+> ```
+
+| Name      | Description                                                                                                                                |
+| --------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| `path`    | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
+| `exclude` | List of components to exclude. ~~Iterable[str]~~                                                                                           |
+
+## InMemoryLookupKB.from_disk {#from_disk tag="method"}
+
+Restore the state of the knowledge base from a given directory. Note that the
+[`Vocab`](/api/vocab) should also be the same as the one used to create the KB.
+
+> #### Example
+>
+> ```python
+> from spacy.vocab import Vocab
+> vocab = Vocab().from_disk("/path/to/vocab")
+> kb = FullyImplementedKB(vocab=vocab, entity_vector_length=64)
+> kb.from_disk("/path/to/kb")
+> ```
+
+| Name        | Description                                                                                     |
+| ----------- | ----------------------------------------------------------------------------------------------- |
+| `loc`       | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
+| `exclude`   | List of components to exclude. ~~Iterable[str]~~                                                |
+| **RETURNS** | The modified `KnowledgeBase` object. ~~KnowledgeBase~~                                          |

website/docs/api/language.md

@@ -64,12 +64,12 @@ spaCy loads a model under the hood based on its
 > ```
 
 | Name                                  | Description                                                                                                                                                                                                                                                                                                        |
-| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `config`                              | The loaded config. ~~Union[Dict[str, Any], Config]~~                                                                                                                                                                                                                                                               |
 | _keyword-only_                        |                                                                                                                                                                                                                                                                                                                    |
 | `vocab`                               | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~                                                                                                                                                                                                                |
-| `disable`                             | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [`nlp.enable_pipe`](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ |
+| `disable`                             | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). Is merged with the config entry `nlp.disabled`. ~~Union[str, Iterable[str]]~~ |
-| `enable` <Tag variant="new">3.4</Tag> | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled, but can be enabled again using [`nlp.enable_pipe`](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~                                  |
+| `enable` <Tag variant="new">3.4</Tag> | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled, but can be enabled again using [nlp.enable_pipe](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~                                                                                  |
 | `exclude`                             | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~                                                                                                                                                           |
 | `meta`                                | [Meta data](/api/data-formats#meta) overrides. ~~Dict[str, Any]~~                                                                                                                                                                                                                                                  |
 | `auto_fill`                           | Whether to automatically fill in missing values in the config, based on defaults and function argument annotations. Defaults to `True`. ~~bool~~                                                                                                                                                                   |
@@ -199,14 +199,14 @@ tokenization is skipped but the rest of the pipeline is run.
 > ```
 
 | Name            | Description                                                                                                                                                         |
-| ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `texts`         | A sequence of strings (or `Doc` objects). ~~Iterable[Union[str, Doc]]~~                                                                                             |
 | _keyword-only_  |                                                                                                                                                                     |
 | `as_tuples`     | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. ~~bool~~ |
 | `batch_size`    | The number of texts to buffer. ~~Optional[int]~~                                                                                                                    |
 | `disable`       | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). ~~List[str]~~                                                                     |
 | `component_cfg` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Any]]]~~                      |
-| `n_process` <Tag variant="new">2.2.2</Tag> | Number of processors to use. Defaults to `1`. ~~int~~                                                                                                               |
+| `n_process`     | Number of processors to use. Defaults to `1`. ~~int~~                                                                                                               |
 | **YIELDS**      | Documents in the order of the original text. ~~Doc~~                                                                                                                |
 
 ## Language.set_error_handler {#set_error_handler tag="method" new="3"}
@@ -1031,20 +1031,20 @@ details.
 ## Attributes {#attributes}
 
 | Name                                         | Description                                                                                                                                    |
-| --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
+| -------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
 | `vocab`                                      | A container for the lexical types. ~~Vocab~~                                                                                                   |
 | `tokenizer`                                  | The tokenizer. ~~Tokenizer~~                                                                                                                   |
 | `make_doc`                                   | Callable that takes a string and returns a `Doc`. ~~Callable[[str], Doc]~~                                                                     |
 | `pipeline`                                   | List of `(name, component)` tuples describing the current processing pipeline, in order. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~            |
-| `pipe_names` <Tag variant="new">2</Tag>       | List of pipeline component names, in order. ~~List[str]~~                                                                                      |
+| `pipe_names`                                 | List of pipeline component names, in order. ~~List[str]~~                                                                                      |
-| `pipe_labels` <Tag variant="new">2.2</Tag>    | List of labels set by the pipeline components, if available, keyed by component name. ~~Dict[str, List[str]]~~                                 |
+| `pipe_labels`                                | List of labels set by the pipeline components, if available, keyed by component name. ~~Dict[str, List[str]]~~                                 |
-| `pipe_factories` <Tag variant="new">2.2</Tag> | Dictionary of pipeline component names, mapped to their factory names. ~~Dict[str, str]~~                                                      |
+| `pipe_factories`                             | Dictionary of pipeline component names, mapped to their factory names. ~~Dict[str, str]~~                                                      |
 | `factories`                                  | All available factory functions, keyed by name. ~~Dict[str, Callable[[...], Callable[[Doc], Doc]]]~~                                           |
 | `factory_names` <Tag variant="new">3</Tag>   | List of all available factory names. ~~List[str]~~                                                                                             |
 | `components` <Tag variant="new">3</Tag>      | List of all available `(name, component)` tuples, including components that are currently disabled. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ |
 | `component_names` <Tag variant="new">3</Tag> | List of all available component names, including components that are currently disabled. ~~List[str]~~                                         |
 | `disabled` <Tag variant="new">3</Tag>        | Names of components that are currently disabled and don't run as part of the pipeline. ~~List[str]~~                                           |
-| `path` <Tag variant="new">2</Tag>             | Path to the pipeline data directory, if a pipeline is loaded from a path or package. Otherwise `None`. ~~Optional[Path]~~                      |
+| `path`                                       | Path to the pipeline data directory, if a pipeline is loaded from a path or package. Otherwise `None`. ~~Optional[Path]~~                      |
 
 ## Class attributes {#class-attributes}
 

website/docs/api/lemmatizer.md

@@ -70,7 +70,7 @@ lemmatizer is available. The lemmatizer modes `rule` and `pos_lookup` require
 [`token.pos`](/api/token) from a previous pipeline component (see example
 pipeline configurations in the
 [pretrained pipeline design details](/models#design-cnn)) or rely on third-party
-libraries (`pymorphy2`).
+libraries (`pymorphy3`).
 
 | Language | Default Mode |
 | -------- | ------------ |
@@ -86,9 +86,9 @@ libraries (`pymorphy2`).
 | `nb`     | `rule`       |
 | `nl`     | `rule`       |
 | `pl`     | `pos_lookup` |
-| `ru`     | `pymorphy2`  |
+| `ru`     | `pymorphy3`  |
 | `sv`     | `rule`       |
-| `uk`     | `pymorphy2`  |
+| `uk`     | `pymorphy3`  |
 
 ```python
 %%GITHUB_SPACY/spacy/pipeline/lemmatizer.py

website/docs/api/lexeme.md

@@ -122,7 +122,7 @@ The L2 norm of the lexeme's vector representation.
 ## Attributes {#attributes}
 
 | Name             | Description                                                                                                                                                                                                                                                          |
-| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `vocab`          | The lexeme's vocabulary. ~~Vocab~~                                                                                                                                                                                                                                   |
 | `text`           | Verbatim text content. ~~str~~                                                                                                                                                                                                                                       |
 | `orth`           | ID of the verbatim text content. ~~int~~                                                                                                                                                                                                                             |
@@ -151,7 +151,7 @@ The L2 norm of the lexeme's vector representation.
 | `is_space`       | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. ~~bool~~                                                                                                                                                                    |
 | `is_bracket`     | Is the lexeme a bracket? ~~bool~~                                                                                                                                                                                                                                    |
 | `is_quote`       | Is the lexeme a quotation mark? ~~bool~~                                                                                                                                                                                                                             |
-| `is_currency` <Tag variant="new">2.0.8</Tag> | Is the lexeme a currency symbol? ~~bool~~                                                                                                                                                                                                                            |
+| `is_currency`    | Is the lexeme a currency symbol? ~~bool~~                                                                                                                                                                                                                            |
 | `like_url`       | Does the lexeme resemble a URL? ~~bool~~                                                                                                                                                                                                                             |
 | `like_num`       | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~                                                                                                                                                                                          |
 | `like_email`     | Does the lexeme resemble an email address? ~~bool~~                                                                                                                                                                                                                  |

website/docs/api/matcher.md

@@ -33,7 +33,7 @@ rule-based matching are:
 | Attribute                                      | Description                                                                                                               |
 | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
 | `ORTH`                                         | The exact verbatim text of a token. ~~str~~                                                                               |
-| `TEXT` <Tag variant="new">2.1</Tag>            | The exact verbatim text of a token. ~~str~~                                                                               |
+| `TEXT`                                         | The exact verbatim text of a token. ~~str~~                                                                               |
 | `NORM`                                         | The normalized form of the token text. ~~str~~                                                                            |
 | `LOWER`                                        | The lowercase form of the token text. ~~str~~                                                                             |
 | `LENGTH`                                       | The length of the token text. ~~int~~                                                                                     |
@@ -48,7 +48,7 @@ rule-based matching are:
 | `ENT_IOB`                                      | The IOB part of the token's entity tag. ~~str~~                                                                           |
 | `ENT_ID`                                       | The token's entity ID (`ent_id`). ~~str~~                                                                                 |
 | `ENT_KB_ID`                                    | The token's entity knowledge base ID (`ent_kb_id`). ~~str~~                                                               |
-| `_` <Tag variant="new">2.1</Tag>               | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~ |
+| `_`                                            | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~ |
 | `OP`                                           | Operator or quantifier to determine how often to match a token pattern. ~~str~~                                           |
 
 Operators and quantifiers define **how often** a token pattern should be
@@ -64,7 +64,7 @@ matched:
 > ```
 
 | OP      | Description                                                            |
-|---------|------------------------------------------------------------------------|
+| ------- | ---------------------------------------------------------------------- |
 | `!`     | Negate the pattern, by requiring it to match exactly 0 times.          |
 | `?`     | Make the pattern optional, by allowing it to match 0 or 1 times.       |
 | `+`     | Require the pattern to match 1 or more times.                          |
@@ -110,9 +110,9 @@ string where an integer is expected) or unexpected property names.
 > ```
 
 | Name       | Description                                                                                           |
-| --------------------------------------- | ----------------------------------------------------------------------------------------------------- |
+| ---------- | ----------------------------------------------------------------------------------------------------- |
 | `vocab`    | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ |
-| `validate` <Tag variant="new">2.1</Tag> | Validate all patterns added to this matcher. ~~bool~~                                                 |
+| `validate` | Validate all patterns added to this matcher. ~~bool~~                                                 |
 
 ## Matcher.\_\_call\_\_ {#call tag="method"}
 

website/docs/api/phrasematcher.md

@@ -37,10 +37,10 @@ be shown.
 > ```
 
 | Name       | Description                                                                                            |
-| --------------------------------------- | ------------------------------------------------------------------------------------------------------ |
+| ---------- | ------------------------------------------------------------------------------------------------------ |
 | `vocab`    | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~  |
-| `attr` <Tag variant="new">2.1</Tag>     | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ |
+| `attr`     | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ |
-| `validate` <Tag variant="new">2.1</Tag> | Validate patterns added to the matcher. ~~bool~~                                                       |
+| `validate` | Validate patterns added to the matcher. ~~bool~~                                                       |
 
 ## PhraseMatcher.\_\_call\_\_ {#call tag="method"}
 

website/docs/api/scorer.md

@@ -230,14 +230,15 @@ The reported `{attr}_score` depends on the classification properties:
 > ```
 
 | Name             | Description                                                                                                                                                                                        |
-| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `examples`       | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~                                                                                |
 | `attr`           | The attribute to score. ~~str~~                                                                                                                                                                    |
 | _keyword-only_   |                                                                                                                                                                                                    |
 | `getter`         | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. ~~Callable[[Doc, str], Dict[str, float]]~~                                                 |
 | labels           | The set of possible labels. Defaults to `[]`. ~~Iterable[str]~~                                                                                                                                    |
-| `multi_label`    | Whether the attribute allows multiple labels. Defaults to `True`. ~~bool~~                                                                         |
+| `multi_label`    | Whether the attribute allows multiple labels. Defaults to `True`. When set to `False` (exclusive labels), missing gold labels are interpreted as `0.0` and the threshold is set to `0.0`. ~~bool~~ |
 | `positive_label` | The positive label for a binary task with exclusive classes. Defaults to `None`. ~~Optional[str]~~                                                                                                 |
+| `threshold`      | Cutoff to consider a prediction "positive". Defaults to `0.5` for multi-label, and `0.0` (i.e. whatever's highest scoring) otherwise. ~~float~~                                                    |
 | **RETURNS**      | A dictionary containing the scores, with inapplicable scores as `None`. ~~Dict[str, Optional[float]]~~                                                                                             |
 
 ## Scorer.score_links {#score_links tag="staticmethod" new="3"}

website/docs/api/span.md

@@ -187,11 +187,11 @@ the character indices don't map to a valid span.
 > ```
 
 | Name        | Description                                                                               |
-| ------------------------------------ | ----------------------------------------------------------------------------------------- |
+| ----------- | ----------------------------------------------------------------------------------------- |
 | `start`     | The index of the first character of the span. ~~int~~                                     |
 | `end`       | The index of the last character after the span. ~~int~~                                   |
 | `label`     | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~               |
-| `kb_id` <Tag variant="new">2.2</Tag> | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
+| `kb_id`     | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
 | `vector`    | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~            |
 | **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~                                |
 
@@ -545,9 +545,9 @@ overlaps with will be returned.
 ## Attributes {#attributes}
 
 | Name           | Description                                                                                                                   |
-| --------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| -------------- | ----------------------------------------------------------------------------------------------------------------------------- |
 | `doc`          | The parent document. ~~Doc~~                                                                                                  |
-| `tensor` <Tag variant="new">2.1.7</Tag> | The span's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~                                                              |
+| `tensor`       | The span's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~                                                              |
 | `start`        | The token offset for the start of the span. ~~int~~                                                                           |
 | `end`          | The token offset for the end of the span. ~~int~~                                                                             |
 | `start_char`   | The character offset for the start of the span. ~~int~~                                                                       |

website/docs/api/spangroup.md

@@ -256,7 +256,8 @@ Return a copy of the span group.
 > ```
 
 | Name        | Description                                                                                        |
-| ----------- | ----------------------------------------------- |
+| ----------- | -------------------------------------------------------------------------------------------------- |
+| `doc`       | The document to which the copy is bound. Defaults to `None` for the current doc. ~~Optional[Doc]~~ |
 | **RETURNS** | A copy of the `SpanGroup` object. ~~SpanGroup~~                                                    |
 
 ## SpanGroup.to_bytes {#to_bytes tag="method"}

website/docs/api/textcategorizer.md

@@ -63,7 +63,6 @@ architectures and their arguments and hyperparameters.
 > ```python
 > from spacy.pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
 > config = {
->    "threshold": 0.5,
 >    "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
 > }
 > nlp.add_pipe("textcat", config=config)
@@ -82,7 +81,7 @@ architectures and their arguments and hyperparameters.
 
 | Setting     | Description                                                                                                                                                      |
 | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~                                                                   |
+| `threshold` | Cutoff to consider a prediction "positive", relevant for `textcat_multilabel` when calculating accuracy scores. ~~float~~                                        |
 | `model`     | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
 | `scorer`    | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~                                 |
 
@@ -123,7 +122,7 @@ shortcut for this and instantiate the component using its string name and
 | `model`        | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~       |
 | `name`         | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                              |
 | _keyword-only_ |                                                                                                                                  |
-| `threshold`    | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~                                   |
+| `threshold`    | Cutoff to consider a prediction "positive", relevant for `textcat_multilabel` when calculating accuracy scores. ~~float~~        |
 | `scorer`       | The scoring method. Defaults to [`Scorer.score_cats`](/api/scorer#score_cats) for the attribute `"cats"`. ~~Optional[Callable]~~ |
 
 ## TextCategorizer.\_\_call\_\_ {#call tag="method"}

website/docs/api/token.md

@@ -404,17 +404,17 @@ The L2 norm of the token's vector representation.
 ## Attributes {#attributes}
 
 | Name                               | Description                                                                                                                                                                                                                                                          |
-| -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `doc`                              | The parent document. ~~Doc~~                                                                                                                                                                                                                                         |
 | `lex` <Tag variant="new">3</Tag>   | The underlying lexeme. ~~Lexeme~~                                                                                                                                                                                                                                    |
-| `sent` <Tag variant="new">2.0.12</Tag>       | The sentence span that this token is a part of. ~~Span~~                                                                                                                                                                                                             |
+| `sent`                             | The sentence span that this token is a part of. ~~Span~~                                                                                                                                                                                                             |
 | `text`                             | Verbatim text content. ~~str~~                                                                                                                                                                                                                                       |
 | `text_with_ws`                     | Text content, with trailing space character if present. ~~str~~                                                                                                                                                                                                      |
 | `whitespace_`                      | Trailing space character if present. ~~str~~                                                                                                                                                                                                                         |
 | `orth`                             | ID of the verbatim text content. ~~int~~                                                                                                                                                                                                                             |
 | `orth_`                            | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. ~~str~~                                                                                                                                                  |
 | `vocab`                            | The vocab object of the parent `Doc`. ~~vocab~~                                                                                                                                                                                                                      |
-| `tensor` <Tag variant="new">2.1.7</Tag>      | The token's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~                                                                                                                                                                                                    |
+| `tensor`                           | The token's slice of the parent `Doc`'s tensor. ~~numpy.ndarray~~                                                                                                                                                                                                    |
 | `head`                             | The syntactic parent, or "governor", of this token. ~~Token~~                                                                                                                                                                                                        |
 | `left_edge`                        | The leftmost token of this token's syntactic descendants. ~~Token~~                                                                                                                                                                                                  |
 | `right_edge`                       | The rightmost token of this token's syntactic descendants. ~~Token~~                                                                                                                                                                                                 |
@@ -423,8 +423,8 @@ The L2 norm of the token's vector representation.
 | `ent_type_`                        | Named entity type. ~~str~~                                                                                                                                                                                                                                           |
 | `ent_iob`                          | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. ~~int~~                                                                                 |
 | `ent_iob_`                         | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. ~~str~~                                                                                  |
-| `ent_kb_id` <Tag variant="new">2.2</Tag>     | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~int~~                                                                                                                                                                           |
+| `ent_kb_id`                        | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~int~~                                                                                                                                                                           |
-| `ent_kb_id_` <Tag variant="new">2.2</Tag>    | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~str~~                                                                                                                                                                           |
+| `ent_kb_id_`                       | Knowledge base ID that refers to the named entity this token is a part of, if any. ~~str~~                                                                                                                                                                           |
 | `ent_id`                           | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. ~~int~~                                                                                                                                        |
 | `ent_id_`                          | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. ~~str~~                                                                                                                                        |
 | `lemma`                            | Base form of the token, with no inflectional suffixes. ~~int~~                                                                                                                                                                                                       |
@@ -453,7 +453,7 @@ The L2 norm of the token's vector representation.
 | `is_space`                         | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. ~~bool~~                                                                                                                                                                      |
 | `is_bracket`                       | Is the token a bracket? ~~bool~~                                                                                                                                                                                                                                     |
 | `is_quote`                         | Is the token a quotation mark? ~~bool~~                                                                                                                                                                                                                              |
-| `is_currency` <Tag variant="new">2.0.8</Tag> | Is the token a currency symbol? ~~bool~~                                                                                                                                                                                                                             |
+| `is_currency`                      | Is the token a currency symbol? ~~bool~~                                                                                                                                                                                                                             |
 | `like_url`                         | Does the token resemble a URL? ~~bool~~                                                                                                                                                                                                                              |
 | `like_num`                         | Does the token represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~                                                                                                                                                                                           |
 | `like_email`                       | Does the token resemble an email address? ~~bool~~                                                                                                                                                                                                                   |

website/docs/api/top-level.md

@@ -46,11 +46,11 @@ specified separately using the new `exclude` keyword argument.
 > ```
 
 | Name                                  | Description                                                                                                                                                                                                                                                                                                        |
-| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `name`                                | Pipeline to load, i.e. package name or path. ~~Union[str, Path]~~                                                                                                                                                                                                                                                  |
 | _keyword-only_                        |                                                                                                                                                                                                                                                                                                                    |
 | `vocab`                               | Optional shared vocab to pass in on initialization. If `True` (default), a new `Vocab` object will be created. ~~Union[Vocab, bool]~~                                                                                                                                                                              |
-| `disable`                             | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). ~~Union[str, Iterable[str]]~~ |
+| `disable`                             | Name(s) of pipeline component(s) to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). Is merged with the config entry `nlp.disabled`. ~~Union[str, Iterable[str]]~~ |
 | `enable` <Tag variant="new">3.4</Tag> | Name(s) of pipeline component(s) to [enable](/usage/processing-pipelines#disabling). All other pipes will be disabled. ~~Union[str, Iterable[str]]~~                                                                                                                                                               |
 | `exclude` <Tag variant="new">3</Tag>  | Name(s) of pipeline component(s) to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~Union[str, Iterable[str]]~~                                                                                                                                                           |
 | `config` <Tag variant="new">3</Tag>   | Optional config overrides, either as nested dict or dict keyed by section value in dot notation, e.g. `"components.name.value"`. ~~Union[Dict[str, Any], Config]~~                                                                                                                                                 |
@@ -355,9 +355,9 @@ If a setting is not present in the options, the default value will be used.
 > ```
 
 | Name               | Description                                                                                                                                  |
-| ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- |
 | `fine_grained`     | Use fine-grained part-of-speech tags (`Token.tag_`) instead of coarse-grained tags (`Token.pos_`). Defaults to `False`. ~~bool~~             |
-| `add_lemma` <Tag variant="new">2.2.4</Tag> | Print the lemmas in a separate row below the token texts. Defaults to `False`. ~~bool~~                                                      |
+| `add_lemma`        | Print the lemmas in a separate row below the token texts. Defaults to `False`. ~~bool~~                                                      |
 | `collapse_punct`   | Attach punctuation to tokens. Can make the parse more readable, as it prevents long arcs to attach punctuation. Defaults to `True`. ~~bool~~ |
 | `collapse_phrases` | Merge noun phrases into one token. Defaults to `False`. ~~bool~~                                                                             |
 | `compact`          | "Compact mode" with square arrows that takes up less space. Defaults to `False`. ~~bool~~                                                    |
@@ -385,7 +385,7 @@ If a setting is not present in the options, the default value will be used.
 | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `ents`                                           | Entity types to highlight or `None` for all types (default). ~~Optional[List[str]]~~                                                                                                                                                        |
 | `colors`                                         | Color overrides. Entity types should be mapped to color names or values. ~~Dict[str, str]~~                                                                                                                                                 |
-| `template` <Tag variant="new">2.2</Tag>          | Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use `{bg}`, `{text}` and `{label}`. See [`templates.py`](%%GITHUB_SPACY/spacy/displacy/templates.py) for examples. ~~Optional[str]~~ |
+| `template`                                       | Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use `{bg}`, `{text}` and `{label}`. See [`templates.py`](%%GITHUB_SPACY/spacy/displacy/templates.py) for examples. ~~Optional[str]~~ |
 | `kb_url_template` <Tag variant="new">3.2.1</Tag> | Optional template to construct the KB url for the entity to link to. Expects a python f-string format with single field to fill in. ~~Optional[str]~~                                                                                       |
 
 #### Span Visualizer options {#displacy_options-span}

website/docs/api/vocab.md

@@ -22,12 +22,12 @@ Create the vocabulary.
 > ```
 
 | Name               | Description                                                                                                                                                             |
-| ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `lex_attr_getters` | A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. ~~Optional[Dict[str, Callable[[str], Any]]]~~                                      |
 | `strings`          | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. ~~Union[List[str], StringStore]~~                           |
 | `lookups`          | A [`Lookups`](/api/lookups) that stores the `lexeme_norm` and other large lookup tables. Defaults to `None`. ~~Optional[Lookups]~~                                      |
 | `oov_prob`         | The default OOV probability. Defaults to `-20.0`. ~~float~~                                                                                                             |
-| `vectors_name` <Tag variant="new">2.2</Tag> | A name to identify the vectors table. ~~str~~                                                                                                                           |
+| `vectors_name`     | A name to identify the vectors table. ~~str~~                                                                                                                           |
 | `writing_system`   | A dictionary describing the language's writing system. Typically provided by [`Language.Defaults`](/api/language#defaults). ~~Dict[str, Any]~~                          |
 | `get_noun_chunks`  | A function that yields base noun phrases used for [`Doc.noun_chunks`](/api/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ |
 
@@ -311,10 +311,10 @@ Load state from a binary string.
 | Name                                           | Description                                                                                                                                                            |
 | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `strings`                                      | A table managing the string-to-int mapping. ~~StringStore~~                                                                                                            |
-| `vectors` <Tag variant="new">2</Tag>           | A table associating word IDs to word vectors. ~~Vectors~~                                                                                                              |
+| `vectors`                                      | A table associating word IDs to word vectors. ~~Vectors~~                                                                                                              |
 | `vectors_length`                               | Number of dimensions for each word vector. ~~int~~                                                                                                                     |
 | `lookups`                                      | The available lookup tables in this vocab. ~~Lookups~~                                                                                                                 |
-| `writing_system` <Tag variant="new">2.1</Tag>  | A dict with information about the language's writing system. ~~Dict[str, Any]~~                                                                                        |
+| `writing_system`                               | A dict with information about the language's writing system. ~~Dict[str, Any]~~                                                                                        |
 | `get_noun_chunks` <Tag variant="new">3.0</Tag> | A function that yields base noun phrases used for [`Doc.noun_chunks`](/ap/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ |
 
 ## Serialization fields {#serialization-fields}

website/docs/usage/101/_architecture.md

@@ -78,7 +78,9 @@ operates on a `Doc` and gives you access to the matched tokens **in context**.
 | Name                                             | Description                                                                                        |
 | ------------------------------------------------ | -------------------------------------------------------------------------------------------------- |
 | [`Corpus`](/api/corpus)                          | Class for managing annotated corpora for training and evaluation data.                             |
-| [`KnowledgeBase`](/api/kb)                       | Storage for entities and aliases of a knowledge base for entity linking.                           |
+| [`KnowledgeBase`](/api/kb)                       | Abstract base class for storage and retrieval of data for entity linking.                          |
+| [`InMemoryLookupKB`](/api/kb_in_memory)          | Implementation of `KnowledgeBase` storing all data in memory.                                      |
+| [`Candidate`](/api/kb#candidate)                 | Object associating a textual mention with a specific entity contained in a `KnowledgeBase`.        |
 | [`Lookups`](/api/lookups)                        | Container for convenient access to large lookup tables and dictionaries.                           |
 | [`MorphAnalysis`](/api/morphology#morphanalysis) | A morphological analysis.                                                                          |
 | [`Morphology`](/api/morphology)                  | Store morphological analyses and map them to and from hash values.                                 |

website/docs/usage/processing-pipelines.md

@@ -363,7 +363,8 @@ nlp.enable_pipe("tagger")
 ```
 
 In addition to `disable`, `spacy.load()` also accepts `enable`. If `enable` is
-set, all components except for those in `enable` are disabled.
+set, all components except for those in `enable` are disabled. If `enable` and
+`disable` conflict (i.e. the same component is included in both), an error is raised.
 
 ```python
 # Load the complete pipeline, but disable all components except for tok2vec and tagger

website/docs/usage/projects.md

@@ -243,6 +243,27 @@ pipelines.
 > python -m spacy project run test . --vars.foo bar
 > ```
 
+> #### Tip: Environment Variables
+>
+> Commands in a project file are not executed in a shell, so they don't have
+> direct access to environment variables. But you can insert environment
+> variables using the `env` dictionary to make values available for
+> interpolation, just like values in `vars`. Here's an example `env` dict that
+> makes `$PATH` available as `ENV_PATH`:
+>
+> ```yaml
+> env:
+>   ENV_PATH: PATH
+> ```
+>
+> This can be used in a project command like so:
+>
+> ```yaml
+>   - name: "echo-path"
+>     script:
+>       - "echo ${env.ENV_PATH}"
+> ```
+
 | Section                                             | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `title`                                             | An optional project title used in `--help` message and [auto-generated docs](#custom-docs).                                                                                                                                                                                                                                                                                                                                                                                                                  |

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

@@ -162,7 +162,7 @@ rule-based matching are:
 | Attribute                                      | Description                                                                                                                                                                                                                                                                                               |
 | ---------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `ORTH`                                         | The exact verbatim text of a token. ~~str~~                                                                                                                                                                                                                                                               |
-| `TEXT` <Tag variant="new">2.1</Tag>            | The exact verbatim text of a token. ~~str~~                                                                                                                                                                                                                                                               |
+| `TEXT`                                         | The exact verbatim text of a token. ~~str~~                                                                                                                                                                                                                                                               |
 | `NORM`                                         | The normalized form of the token text. ~~str~~                                                                                                                                                                                                                                                            |
 | `LOWER`                                        | The lowercase form of the token text. ~~str~~                                                                                                                                                                                                                                                             |
 | `LENGTH`                                       | The length of the token text. ~~int~~                                                                                                                                                                                                                                                                     |
@@ -174,7 +174,7 @@ rule-based matching are:
 | `SPACY`                                        | Token has a trailing space. ~~bool~~                                                                                                                                                                                                                                                                      |
 | `POS`, `TAG`, `MORPH`, `DEP`, `LEMMA`, `SHAPE` | The token's simple and extended part-of-speech tag, morphological analysis, dependency label, lemma, shape. Note that the values of these attributes are case-sensitive. For a list of available part-of-speech tags and dependency labels, see the [Annotation Specifications](/api/annotation). ~~str~~ |
 | `ENT_TYPE`                                     | The token's entity label. ~~str~~                                                                                                                                                                                                                                                                         |
-| `_` <Tag variant="new">2.1</Tag>               | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~                                                                                                                                                                                 |
+| `_`                                            | Properties in [custom extension attributes](/usage/processing-pipelines#custom-components-attributes). ~~Dict[str, Any]~~                                                                                                                                                                                 |
 | `OP`                                           | [Operator or quantifier](#quantifiers) to determine how often to match a token pattern. ~~str~~                                                                                                                                                                                                           |
 
 <Accordion title="Does it matter if the attribute names are uppercase or lowercase?">
@@ -375,7 +375,7 @@ scoped quantifiers – instead, you can build those behaviors with `on_match`
 callbacks.
 
 | OP      | Description                                                            |
-|---------|------------------------------------------------------------------------|
+| ------- | ---------------------------------------------------------------------- |
 | `!`     | Negate the pattern, by requiring it to match exactly 0 times.          |
 | `?`     | Make the pattern optional, by allowing it to match 0 or 1 times.       |
 | `+`     | Require the pattern to match 1 or more times.                          |
@@ -1792,7 +1792,7 @@ the entity `Span` – for example `._.orgs` or `._.prev_orgs` and
 > [`Doc.retokenize`](/api/doc#retokenize) context manager:
 >
 > ```python
-> with doc.retokenize() as retokenize:
+> with doc.retokenize() as retokenizer:
 >   for ent in doc.ents:
 >       retokenizer.merge(ent)
 > ```

website/docs/usage/saving-loading.md

@@ -307,11 +307,11 @@ use your entry points, your package needs to expose them and it needs to be
 installed in the same environment – that's it.
 
 | Entry point                                       | Description                                                                                                                                                                                                                                              |
-| ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | [`spacy_factories`](#entry-points-components)     | Group of entry points for pipeline component factories, keyed by component name. Can be used to expose custom components defined by another package.                                                                                                     |
 | [`spacy_languages`](#entry-points-languages)      | Group of entry points for custom [`Language` subclasses](/usage/linguistic-features#language-data), keyed by language shortcut.                                                                                                                          |
-| `spacy_lookups` <Tag variant="new">2.2</Tag>                                   | Group of entry points for custom [`Lookups`](/api/lookups), including lemmatizer data. Used by spaCy's [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package.                                                                  |
+| `spacy_lookups`                                   | Group of entry points for custom [`Lookups`](/api/lookups), including lemmatizer data. Used by spaCy's [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package.                                                                  |
-| [`spacy_displacy_colors`](#entry-points-displacy) <Tag variant="new">2.2</Tag> | Group of entry points of custom label colors for the [displaCy visualizer](/usage/visualizers#ent). The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. |
+| [`spacy_displacy_colors`](#entry-points-displacy) | Group of entry points of custom label colors for the [displaCy visualizer](/usage/visualizers#ent). The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. |
 
 ### Custom components via entry points {#entry-points-components}
 

website/docs/usage/training.md

@@ -480,7 +480,7 @@ as-is. They are also excluded when calling
 > parse. So the evaluation results should always reflect what your pipeline will
 > produce at runtime. If you want a frozen component to run (without updating)
 > during training as well, so that downstream components can use its
-> **predictions**, you can add it to the list of
+> **predictions**, you should add it to the list of
 > [`annotating_components`](/usage/training#annotating-components).
 
 ```ini

website/meta/languages.json

@@ -4,12 +4,22 @@
             "code": "af",
             "name": "Afrikaans"
         },
+        {
+            "code": "am",
+            "name": "Amharic",
+            "has_examples": true
+        },
         {
             "code": "ar",
             "name": "Arabic",
             "example": "هذه جملة",
             "has_examples": true
         },
+        {
+            "code": "az",
+            "name": "Azerbaijani",
+            "has_examples": true
+        },
         {
             "code": "bg",
             "name": "Bulgarian",
@@ -142,6 +152,11 @@
             "code": "ga",
             "name": "Irish"
         },
+        {
+            "code": "grc",
+            "name": "Ancient Greek",
+            "has_examples": true
+        },
         {
             "code": "gu",
             "name": "Gujarati",
@@ -260,6 +275,10 @@
             "example": "Адамга эң кыйыны — күн сайын адам болуу",
             "has_examples": true
         },
+        {
+            "code": "la",
+            "name": "Latin"
+        },
         {
             "code": "lb",
             "name": "Luxembourgish",
@@ -374,8 +393,8 @@
             "has_examples": true,
             "dependencies": [
                 {
-                    "name": "pymorphy2",
+                    "name": "pymorphy3",
-                    "url": "https://github.com/kmike/pymorphy2"
+                    "url": "https://github.com/no-plagiarism/pymorphy3"
                 }
             ],
             "models": [
@@ -448,6 +467,11 @@
             "example": "นี่คือประโยค",
             "has_examples": true
         },
+        {
+            "code": "ti",
+            "name": "Tigrinya",
+            "has_examples": true
+        },
         {
             "code": "tl",
             "name": "Tagalog"
@@ -480,12 +504,12 @@
             ],
             "dependencies": [
                 {
-                    "name": "pymorphy2",
+                    "name": "pymorphy3",
-                    "url": "https://github.com/kmike/pymorphy2"
+                    "url": "https://github.com/no-plagiarism/pymorphy3"
                 },
                 {
-                    "name": "pymorphy2-dicts-uk",
+                    "name": "pymorphy3-dicts-uk",
-                    "url": "https://github.com/kmike/pymorphy2-dicts/"
+                    "url": "https://github.com/no-plagiarism/pymorphy3-dicts"
                 }
             ]
         },

website/meta/universe.json

@@ -1,5 +1,72 @@
 {
     "resources": [
+         {
+            "id": "grecy",
+            "title": "greCy",
+            "slogan": "Ancient Greek pipelines for spaCy",
+            "description": "greCy offers state-of-the-art pipelines for ancient Greek NLP. The repository makes language models available in various sizes, some of them containing floret word vectors and a BERT transformer layer.",
+            "github": "jmyerston/greCy",
+            "code_example": [
+                "import spacy",
+                "#After installing the grc_ud_proiel_trf wheel package from the greCy repository",
+                "",
+                "nlp = spacy.load('grc_ud_proiel_trf')",
+                "doc = nlp('δοκῶ μοι περὶ ὧν πυνθάνεσθε οὐκ ἀμελέτητος εἶναι.')",
+                "",
+                "for token in doc:",
+                "   print(token.text, token.norm_, token.lemma_, token.pos_, token.tag_)"
+            ],
+            "code_language": "python",
+            "author": "Jacobo Myerston",
+            "author_links": {
+                "twitter": "@jcbmyrstn",
+                "github": "jmyerston",
+                "website": "https://huggingface.co/spaces/Jacobo/syntax"
+            },
+            "category": ["pipeline", "research"],
+            "tags": ["ancient Greek"]
+        },
+        {
+            "id": "spacy-cleaner",
+            "title": "spacy-cleaner",
+            "slogan": "Easily clean text with spaCy!",
+            "description": "**spacy-cleaner** utilises spaCy `Language` models to replace, remove, and \n  mutate spaCy tokens. Cleaning actions available are:\n\n* Remove/replace stopwords.\n* Remove/replace punctuation.\n* Remove/replace numbers.\n* Remove/replace emails.\n* Remove/replace URLs.\n* Perform lemmatisation.\n\nSee our [docs](https://ce11an.github.io/spacy-cleaner/) for more information.",
+            "github": "Ce11an/spacy-cleaner",
+            "pip": "spacy-cleaner",
+            "code_example": [
+                "import spacy",
+                "import spacy_cleaner",
+                "from spacy_cleaner.processing import removers, replacers, mutators",
+                "",
+                "model = spacy.load(\"en_core_web_sm\")",
+                "pipeline = spacy_cleaner.Pipeline(",
+                "    model,",
+                "    removers.remove_stopword_token,",
+                "    replacers.replace_punctuation_token,",
+                "    mutators.mutate_lemma_token,",
+                ")",
+                "",
+                "texts = [\"Hello, my name is Cellan! I love to swim!\"]",
+                "",
+                "pipeline.clean(texts)",
+                "# ['hello _IS_PUNCT_ Cellan _IS_PUNCT_ love swim _IS_PUNCT_']"
+            ],
+            "code_language": "python",
+            "url": "https://ce11an.github.io/spacy-cleaner/",
+            "image": "https://raw.githubusercontent.com/Ce11an/spacy-cleaner/main/docs/assets/images/spacemen.png",
+            "author": "Cellan Hall",
+            "author_links": {
+                "twitter": "Ce11an",
+                "github": "Ce11an",
+                "website": "https://www.linkedin.com/in/cellan-hall/"
+            },
+            "category": [
+                "extension"
+            ],
+            "tags": [
+                "text-processing"
+            ]
+        },
         {
             "id": "Zshot",
             "title": "Zshot",

website/src/styles/quickstart.module.sass

@@ -149,6 +149,9 @@
     & > span
         display: block
 
+    a
+        text-decoration: underline
+
 .small
     font-size: var(--font-size-code)
     line-height: 1.65

website/src/widgets/quickstart-install.js

@@ -159,6 +159,9 @@ const QuickstartInstall = ({ id, title }) => {
                         setters={setters}
                         showDropdown={showDropdown}
                     >
+                        <QS os="mac" hardware="gpu" platform="arm">
+                            # Note M1 GPU support is experimental, see <a href="https://github.com/explosion/thinc/issues/792">Thinc issue #792</a>
+                        </QS>
                         <QS package="pip" config="venv">
                             python -m venv .env
                         </QS>
@@ -198,7 +201,13 @@ const QuickstartInstall = ({ id, title }) => {
                             {nightly ? ' --pre' : ''}
                         </QS>
                         <QS package="conda">conda install -c conda-forge spacy</QS>
-                        <QS package="conda" hardware="gpu">
+                        <QS package="conda" hardware="gpu" os="windows">
+                            conda install -c conda-forge cupy
+                        </QS>
+                        <QS package="conda" hardware="gpu" os="linux">
+                            conda install -c conda-forge cupy
+                        </QS>
+                        <QS package="conda" hardware="gpu" os="mac" platform="x86">
                             conda install -c conda-forge cupy
                         </QS>
                         <QS package="conda" config="train">