Merge remote-tracking branch 'upstream/master' into develop

.github/FUNDING.yml

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

README.md

@@ -39,28 +39,35 @@ open-source software, released under the
 | 🚀 **[New in v3.0]**                                                                                                                                                                                                      | New features, backwards incompatibilities and migration guide.                                                                                                                                                                                                                                                                               |
 | 🪐 **[Project Templates]**                                                                                                                                                                                                | End-to-end workflows you can clone, modify and run.                                                                                                                                                                                                                                                                                          |
 | 🎛 **[API Reference]**                                                                                                                                                                                                     | The detailed reference for spaCy's API.                                                                                                                                                                                                                                                                                                      |
+| ⏩ **[GPU Processing]**                                                                                                                                                                                                    | Use spaCy with CUDA-compatible GPU processing.                                                                                                                                                                                                                                                                                               |
 | 📦 **[Models]**                                                                                                                                                                                                           | Download trained pipelines for spaCy.                                                                                                                                                                                                                                                                                                        |
+| 🦙 **[Large Language Models]**                                                                                                                                                                                            | Integrate LLMs into spaCy pipelines.                                                                                                                                                                                                                                                                                                        |
 | 🌌 **[Universe]**                                                                                                                                                                                                         | Plugins, extensions, demos and books from the spaCy ecosystem.                                                                                                                                                                                                                                                                               |
 | ⚙️ **[spaCy VS Code Extension]**                                                                                                                                                                                          | Additional tooling and features for working with spaCy's config files.                                                                                                                                                                                                                                                                       |
 | 👩‍🏫 **[Online Course]**                                                                                                                                                                                                    | Learn spaCy in this free and interactive online course.                                                                                                                                                                                                                                                                                      |
+| 📰 **[Blog]**                                                                                                                                                                                                             | Read about current spaCy and Prodigy development, releases, talks and more from Explosion.                                                                                                                                                                                                                 |
 | 📺 **[Videos]**                                                                                                                                                                                                           | Our YouTube channel with video tutorials, talks and more.                                                                                                                                                                                                                                                                                    |
 | 🛠 **[Changelog]**                                                                                                                                                                                                         | Changes and version history.                                                                                                                                                                                                                                                                                                                 |
 | 💝 **[Contribute]**                                                                                                                                                                                                       | How to contribute to the spaCy project and code base.                                                                                                                                                                                                                                                                                        |
-| <a href="https://explosion.ai/spacy-tailored-pipelines"><img src="https://user-images.githubusercontent.com/13643239/152853098-1c761611-ccb0-4ec6-9066-b234552831fe.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-pipelines)**                 |
-| <a href="https://explosion.ai/spacy-tailored-analysis"><img src="https://user-images.githubusercontent.com/1019791/206151300-b00cd189-e503-4797-aa1e-1bb6344062c5.png" width="125" alt="spaCy Tailored Pipelines"/></a>   | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-analysis)** |
+| 👕 **[Swag]**                                                                                                                                                                                                             | Support us and our work with unique, custom-designed swag!                                                                                                                                                                                                                                                                                   |
+| <a href="https://explosion.ai/tailored-solutions"><img src="https://github.com/explosion/spaCy/assets/13643239/36d2a42e-98c0-4599-90e1-788ef75181be" width="150" alt="Tailored Solutions"/></a> | Custom NLP consulting, implementation and strategic advice by spaCy’s core development team. Streamlined, production-ready, predictable and maintainable. Send us an email or take our 5-minute questionnaire, and well'be in touch! **[Learn more &rarr;](https://explosion.ai/tailored-solutions)**                 |
 
 [spacy 101]: https://spacy.io/usage/spacy-101
 [new in v3.0]: https://spacy.io/usage/v3
 [usage guides]: https://spacy.io/usage/
 [api reference]: https://spacy.io/api/
+[gpu processing]: https://spacy.io/usage#gpu
 [models]: https://spacy.io/models
+[large language models]: https://spacy.io/usage/large-language-models
 [universe]: https://spacy.io/universe
 [spacy vs code extension]: https://github.com/explosion/spacy-vscode
 [videos]: https://www.youtube.com/c/ExplosionAI
 [online course]: https://course.spacy.io
+[blog]: https://explosion.ai
 [project templates]: https://github.com/explosion/projects
 [changelog]: https://spacy.io/usage#changelog
 [contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
+[swag]: https://explosion.ai/merch
 
 ## 💬 Where to ask questions
 

licenses/3rd_party_licenses.txt

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

pyproject.toml

@@ -5,7 +5,7 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.1.8,<8.3.0",
+    "thinc>=8.2.2,<8.3.0",
     "numpy>=1.15.0; python_version < '3.9'",
     "numpy>=1.25.0; python_version >= '3.9'",
 ]

requirements.txt

@@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.1.8,<8.3.0
+thinc>=8.2.2,<8.3.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0

setup.cfg

@@ -41,7 +41,7 @@ setup_requires =
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.1.8,<8.3.0
+    thinc>=8.2.2,<8.3.0
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.11,<3.1.0
@@ -49,7 +49,7 @@ install_requires =
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.1.8,<8.3.0
+    thinc>=8.2.2,<8.3.0
     wasabi>=0.9.1,<1.2.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0

spacy/cli/download.py

@@ -7,7 +7,14 @@ from wasabi import msg
 
 from .. import about
 from ..errors import OLD_MODEL_SHORTCUTS
-from ..util import get_minor_version, is_package, is_prerelease_version, run_command
+from ..util import (
+    get_minor_version,
+    is_in_interactive,
+    is_in_jupyter,
+    is_package,
+    is_prerelease_version,
+    run_command,
+)
 from ._util import SDIST_SUFFIX, WHEEL_SUFFIX, Arg, Opt, app
 
 
@@ -77,6 +84,27 @@ def download(
         "Download and installation successful",
         f"You can now load the package via spacy.load('{model_name}')",
     )
+    if is_in_jupyter():
+        reload_deps_msg = (
+            "If you are in a Jupyter or Colab notebook, you may need to "
+            "restart Python in order to load all the package's dependencies. "
+            "You can do this by selecting the 'Restart kernel' or 'Restart "
+            "runtime' option."
+        )
+        msg.warn(
+            "Restart to reload dependencies",
+            reload_deps_msg,
+        )
+    elif is_in_interactive():
+        reload_deps_msg = (
+            "If you are in an interactive Python session, you may need to "
+            "exit and restart Python to load all the package's dependencies. "
+            "You can exit with Ctrl-D (or Ctrl-Z and Enter on Windows)."
+        )
+        msg.warn(
+            "Restart to reload dependencies",
+            reload_deps_msg,
+        )
 
 
 def get_model_filename(model_name: str, version: str, sdist: bool = False) -> str:

spacy/cli/package.py

@@ -1,5 +1,7 @@
+import os
 import re
 import shutil
+import subprocess
 import sys
 from collections import defaultdict
 from pathlib import Path
@@ -11,6 +13,7 @@ from thinc.api import Config
 from wasabi import MarkdownRenderer, Printer, get_raw_input
 
 from .. import about, util
+from ..compat import importlib_metadata
 from ..schemas import ModelMetaSchema, validate
 from ._util import SDIST_SUFFIX, WHEEL_SUFFIX, Arg, Opt, app, string_to_list
 
@@ -35,7 +38,7 @@ def package_cli(
     specified output directory, and the data will be copied over. If
     --create-meta is set and a meta.json already exists in the output directory,
     the existing values will be used as the defaults in the command-line prompt.
-    After packaging, "python setup.py sdist" is run in the package directory,
+    After packaging, "python -m build --sdist" is run in the package directory,
     which will create a .tar.gz archive that can be installed via "pip install".
 
     If additional code files are provided (e.g. Python files containing custom
@@ -78,9 +81,17 @@ def package(
     input_path = util.ensure_path(input_dir)
     output_path = util.ensure_path(output_dir)
     meta_path = util.ensure_path(meta_path)
-    if create_wheel and not has_wheel():
-        err = "Generating a binary .whl file requires wheel to be installed"
-        msg.fail(err, "pip install wheel", exits=1)
+    if create_wheel and not has_wheel() and not has_build():
+        err = (
+            "Generating wheels requires 'build' or 'wheel' (deprecated) to be installed"
+        )
+        msg.fail(err, "pip install build", exits=1)
+    if not has_build():
+        msg.warn(
+            "Generating packages without the 'build' package is deprecated and "
+            "will not be supported in the future. To install 'build': pip "
+            "install build"
+        )
     if not input_path or not input_path.exists():
         msg.fail("Can't locate pipeline data", input_path, exits=1)
     if not output_path or not output_path.exists():
@@ -184,12 +195,37 @@ def package(
     msg.good(f"Successfully created package directory '{model_name_v}'", main_path)
     if create_sdist:
         with util.working_dir(main_path):
-            util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
+            # run directly, since util.run_command is not designed to continue
+            # after a command fails
+            ret = subprocess.run(
+                [sys.executable, "-m", "build", ".", "--sdist"],
+                env=os.environ.copy(),
+            )
+            if ret.returncode != 0:
+                msg.warn(
+                    "Creating sdist with 'python -m build' failed. Falling "
+                    "back to deprecated use of 'python setup.py sdist'"
+                )
+                util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
         zip_file = main_path / "dist" / f"{model_name_v}{SDIST_SUFFIX}"
         msg.good(f"Successfully created zipped Python package", zip_file)
     if create_wheel:
         with util.working_dir(main_path):
-            util.run_command([sys.executable, "setup.py", "bdist_wheel"], capture=False)
+            # run directly, since util.run_command is not designed to continue
+            # after a command fails
+            ret = subprocess.run(
+                [sys.executable, "-m", "build", ".", "--wheel"],
+                env=os.environ.copy(),
+            )
+            if ret.returncode != 0:
+                msg.warn(
+                    "Creating wheel with 'python -m build' failed. Falling "
+                    "back to deprecated use of 'wheel' with "
+                    "'python setup.py bdist_wheel'"
+                )
+                util.run_command(
+                    [sys.executable, "setup.py", "bdist_wheel"], capture=False
+                )
         wheel_name_squashed = re.sub("_+", "_", model_name_v)
         wheel = main_path / "dist" / f"{wheel_name_squashed}{WHEEL_SUFFIX}"
         msg.good(f"Successfully created binary wheel", wheel)
@@ -209,6 +245,17 @@ def has_wheel() -> bool:
         return False
 
 
+def has_build() -> bool:
+    # it's very likely that there is a local directory named build/ (especially
+    # in an editable install), so an import check is not sufficient; instead
+    # check that there is a package version
+    try:
+        importlib_metadata.version("build")
+        return True
+    except importlib_metadata.PackageNotFoundError:  # type: ignore[attr-defined]
+        return False
+
+
 def get_third_party_dependencies(
     config: Config, exclude: List[str] = util.SimpleFrozenList()
 ) -> List[str]:

spacy/cli/templates/quickstart_training.jinja

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

spacy/errors.py

@@ -227,7 +227,6 @@ class Errors(metaclass=ErrorsWithCodes):
     E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). "
             "This usually happens when spaCy calls `nlp.{method}` with a custom "
             "component name that's not registered on the current language class. "
-            "If you're using a Transformer, make sure to install 'spacy-transformers'. "
             "If you're using a custom component, make sure you've added the "
             "decorator `@Language.component` (for function components) or "
             "`@Language.factory` (for class components).\n\nAvailable "
@@ -984,6 +983,10 @@ class Errors(metaclass=ErrorsWithCodes):
              "predicted docs when training {component}.")
     E1055 = ("The 'replace_listener' callback expects {num_params} parameters, "
              "but only callbacks with one or three parameters are supported")
+    E1056 = ("The `TextCatBOW` architecture expects a length of at least 1, was {length}.")
+    E1057 = ("The `TextCatReduce` architecture must be used with at least one "
+             "reduction. Please enable one of `use_reduce_first`, "
+             "`use_reduce_last`, `use_reduce_max` or `use_reduce_mean`.")
 
 
 # Deprecated model shortcuts, only used in errors and warnings

spacy/lang/en/lex_attrs.py

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

spacy/lang/fo/__init__.py

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

spacy/lang/fo/tokenizer_exceptions.py

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

spacy/lang/nn/__init__.py

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

spacy/lang/nn/examples.py

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

spacy/lang/nn/punctuation.py

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

spacy/lang/nn/tokenizer_exceptions.py

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

spacy/language.py

@@ -1683,6 +1683,12 @@ class Language:
         for proc in procs:
             proc.start()
 
+        # Close writing-end of channels. This is needed to avoid that reading
+        # from the channel blocks indefinitely when the worker closes the
+        # channel.
+        for tx in bytedocs_send_ch:
+            tx.close()
+
         # Cycle channels not to break the order of docs.
         # The received object is a batch of byte-encoded docs, so flatten them with chain.from_iterable.
         byte_tuples = chain.from_iterable(
@@ -1705,8 +1711,23 @@ class Language:
                     # tell `sender` that one batch was consumed.
                     sender.step()
         finally:
+            # If we are stopping in an orderly fashion, the workers' queues
+            # are empty. Put the sentinel in their queues to signal that work
+            # is done, so that they can exit gracefully.
+            for q in texts_q:
+                q.put(_WORK_DONE_SENTINEL)
+
+            # Otherwise, we are stopping because the error handler raised an
+            # exception. The sentinel will be last to go out of the queue.
+            # To avoid doing unnecessary work or hanging on platforms that
+            # block on sending (Windows), we'll close our end of the channel.
+            # This signals to the worker that it can exit the next time it
+            # attempts to send data down the channel.
+            for r in bytedocs_recv_ch:
+                r.close()
+
             for proc in procs:
-                proc.terminate()
+                proc.join()
 
     def _link_components(self) -> None:
         """Register 'listeners' within pipeline components, to allow them to
@@ -2323,6 +2344,11 @@ def _apply_pipes(
     while True:
         try:
             texts_with_ctx = receiver.get()
+
+            # Stop working if we encounter the end-of-work sentinel.
+            if isinstance(texts_with_ctx, _WorkDoneSentinel):
+                return
+
             docs = (
                 ensure_doc(doc_like, context) for doc_like, context in texts_with_ctx
             )
@@ -2331,11 +2357,21 @@ def _apply_pipes(
             # Connection does not accept unpickable objects, so send list.
             byte_docs = [(doc.to_bytes(), doc._context, None) for doc in docs]
             padding = [(None, None, None)] * (len(texts_with_ctx) - len(byte_docs))
-            sender.send(byte_docs + padding)  # type: ignore[operator]
+            data: Sequence[Tuple[Optional[bytes], Optional[Any], Optional[bytes]]] = (
+                byte_docs + padding  # type: ignore[operator]
+            )
         except Exception:
             error_msg = [(None, None, srsly.msgpack_dumps(traceback.format_exc()))]
             padding = [(None, None, None)] * (len(texts_with_ctx) - 1)
-            sender.send(error_msg + padding)
+            data = error_msg + padding
+
+        try:
+            sender.send(data)
+        except BrokenPipeError:
+            # Parent has closed the pipe prematurely. This happens when a
+            # worker encounters an error and the error handler is set to
+            # stop processing.
+            return
 
 
 class _Sender:
@@ -2365,3 +2401,10 @@ class _Sender:
         if self.count >= self.chunk_size:
             self.count = 0
             self.send()
+
+
+class _WorkDoneSentinel:
+    pass
+
+
+_WORK_DONE_SENTINEL = _WorkDoneSentinel()

spacy/ml/models/textcat.py

@@ -1,21 +1,27 @@
 from functools import partial
-from typing import List, Optional, cast
+from typing import List, Optional, Tuple, cast
 
 from thinc.api import (
     Dropout,
+    Gelu,
     LayerNorm,
     Linear,
     Logistic,
     Maxout,
     Model,
     ParametricAttention,
+    ParametricAttention_v2,
     Relu,
     Softmax,
     SparseLinear,
+    SparseLinear_v2,
     chain,
     clone,
     concatenate,
     list2ragged,
+    reduce_first,
+    reduce_last,
+    reduce_max,
     reduce_mean,
     reduce_sum,
     residual,
@@ -25,9 +31,10 @@ from thinc.api import (
 )
 from thinc.layers.chain import init as init_chain
 from thinc.layers.resizable import resize_linear_weighted, resize_model
-from thinc.types import Floats2d
+from thinc.types import ArrayXd, Floats2d
 
 from ...attrs import ORTH
+from ...errors import Errors
 from ...tokens import Doc
 from ...util import registry
 from ..extract_ngrams import extract_ngrams
@@ -47,39 +54,15 @@ def build_simple_cnn_text_classifier(
     outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
     is applied instead, so that outputs are in the range [0, 1].
     """
-    fill_defaults = {"b": 0, "W": 0}
-    with Model.define_operators({">>": chain}):
-        cnn = tok2vec >> list2ragged() >> reduce_mean()
-        nI = tok2vec.maybe_get_dim("nO")
-        if exclusive_classes:
-            output_layer = Softmax(nO=nO, nI=nI)
-            fill_defaults["b"] = NEG_VALUE
-            resizable_layer: Model = resizable(
-                output_layer,
-                resize_layer=partial(
-                    resize_linear_weighted, fill_defaults=fill_defaults
-                ),
-            )
-            model = cnn >> resizable_layer
-        else:
-            output_layer = Linear(nO=nO, nI=nI)
-            resizable_layer = resizable(
-                output_layer,
-                resize_layer=partial(
-                    resize_linear_weighted, fill_defaults=fill_defaults
-                ),
-            )
-            model = cnn >> resizable_layer >> Logistic()
-        model.set_ref("output_layer", output_layer)
-        model.attrs["resize_output"] = partial(
-            resize_and_set_ref,
-            resizable_layer=resizable_layer,
-        )
-    model.set_ref("tok2vec", tok2vec)
-    if nO is not None:
-        model.set_dim("nO", cast(int, nO))
-    model.attrs["multi_label"] = not exclusive_classes
-    return model
+    return build_reduce_text_classifier(
+        tok2vec=tok2vec,
+        exclusive_classes=exclusive_classes,
+        use_reduce_first=False,
+        use_reduce_last=False,
+        use_reduce_max=False,
+        use_reduce_mean=True,
+        nO=nO,
+    )
 
 
 def resize_and_set_ref(model, new_nO, resizable_layer):
@@ -95,10 +78,48 @@ def build_bow_text_classifier(
     ngram_size: int,
     no_output_layer: bool,
     nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    return _build_bow_text_classifier(
+        exclusive_classes=exclusive_classes,
+        ngram_size=ngram_size,
+        no_output_layer=no_output_layer,
+        nO=nO,
+        sparse_linear=SparseLinear(nO=nO),
+    )
+
+
+@registry.architectures("spacy.TextCatBOW.v3")
+def build_bow_text_classifier_v3(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    length: int = 262144,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    if length < 1:
+        raise ValueError(Errors.E1056.format(length=length))
+
+    # Find k such that 2**(k-1) < length <= 2**k.
+    length = 2 ** (length - 1).bit_length()
+
+    return _build_bow_text_classifier(
+        exclusive_classes=exclusive_classes,
+        ngram_size=ngram_size,
+        no_output_layer=no_output_layer,
+        nO=nO,
+        sparse_linear=SparseLinear_v2(nO=nO, length=length),
+    )
+
+
+def _build_bow_text_classifier(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    sparse_linear: Model[Tuple[ArrayXd, ArrayXd, ArrayXd], ArrayXd],
+    nO: Optional[int] = None,
 ) -> Model[List[Doc], Floats2d]:
     fill_defaults = {"b": 0, "W": 0}
     with Model.define_operators({">>": chain}):
-        sparse_linear = SparseLinear(nO=nO)
         output_layer = None
         if not no_output_layer:
             fill_defaults["b"] = NEG_VALUE
@@ -127,6 +148,9 @@ def build_text_classifier_v2(
     linear_model: Model[List[Doc], Floats2d],
     nO: Optional[int] = None,
 ) -> Model[List[Doc], Floats2d]:
+    # TODO: build the model with _build_parametric_attention_with_residual_nonlinear
+    # in spaCy v4. We don't do this in spaCy v3 to preserve model
+    # compatibility.
     exclusive_classes = not linear_model.attrs["multi_label"]
     with Model.define_operators({">>": chain, "|": concatenate}):
         width = tok2vec.maybe_get_dim("nO")
@@ -190,3 +214,145 @@ def build_text_classifier_lowdata(
             model = model >> Dropout(dropout)
         model = model >> Logistic()
     return model
+
+
+@registry.architectures("spacy.TextCatParametricAttention.v1")
+def build_textcat_parametric_attention_v1(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    exclusive_classes: bool,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    width = tok2vec.maybe_get_dim("nO")
+    parametric_attention = _build_parametric_attention_with_residual_nonlinear(
+        tok2vec=tok2vec,
+        nonlinear_layer=Maxout(nI=width, nO=width),
+        key_transform=Gelu(nI=width, nO=width),
+    )
+    with Model.define_operators({">>": chain}):
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO)
+        else:
+            output_layer = Linear(nO=nO) >> Logistic()
+        model = parametric_attention >> output_layer
+    if model.has_dim("nO") is not False and nO is not None:
+        model.set_dim("nO", cast(int, nO))
+    model.set_ref("output_layer", output_layer)
+    model.attrs["multi_label"] = not exclusive_classes
+
+    return model
+
+
+def _build_parametric_attention_with_residual_nonlinear(
+    *,
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    nonlinear_layer: Model[Floats2d, Floats2d],
+    key_transform: Optional[Model[Floats2d, Floats2d]] = None,
+) -> Model[List[Doc], Floats2d]:
+    with Model.define_operators({">>": chain, "|": concatenate}):
+        width = tok2vec.maybe_get_dim("nO")
+        attention_layer = ParametricAttention_v2(nO=width, key_transform=key_transform)
+        norm_layer = LayerNorm(nI=width)
+        parametric_attention = (
+            tok2vec
+            >> list2ragged()
+            >> attention_layer
+            >> reduce_sum()
+            >> residual(nonlinear_layer >> norm_layer >> Dropout(0.0))
+        )
+
+        parametric_attention.init = _init_parametric_attention_with_residual_nonlinear
+
+        parametric_attention.set_ref("tok2vec", tok2vec)
+        parametric_attention.set_ref("attention_layer", attention_layer)
+        parametric_attention.set_ref("nonlinear_layer", nonlinear_layer)
+        parametric_attention.set_ref("norm_layer", norm_layer)
+
+        return parametric_attention
+
+
+def _init_parametric_attention_with_residual_nonlinear(model, X, Y) -> Model:
+    tok2vec_width = get_tok2vec_width(model)
+    model.get_ref("attention_layer").set_dim("nO", tok2vec_width)
+    model.get_ref("nonlinear_layer").set_dim("nO", tok2vec_width)
+    model.get_ref("nonlinear_layer").set_dim("nI", tok2vec_width)
+    model.get_ref("norm_layer").set_dim("nI", tok2vec_width)
+    model.get_ref("norm_layer").set_dim("nO", tok2vec_width)
+    init_chain(model, X, Y)
+    return model
+
+
+@registry.architectures("spacy.TextCatReduce.v1")
+def build_reduce_text_classifier(
+    tok2vec: Model,
+    exclusive_classes: bool,
+    use_reduce_first: bool,
+    use_reduce_last: bool,
+    use_reduce_max: bool,
+    use_reduce_mean: bool,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    """Build a model that classifies pooled `Doc` representations.
+
+    Pooling is performed using reductions. Reductions are concatenated when
+    multiple reductions are used.
+
+    tok2vec (Model): the tok2vec layer to pool over.
+    exclusive_classes (bool): Whether or not classes are mutually exclusive.
+    use_reduce_first (bool): Pool by using the hidden representation of the
+        first token of a `Doc`.
+    use_reduce_last (bool): Pool by using the hidden representation of the
+        last token of a `Doc`.
+    use_reduce_max (bool): Pool by taking the maximum values of the hidden
+        representations of a `Doc`.
+    use_reduce_mean (bool): Pool by taking the mean of all hidden
+        representations of a `Doc`.
+    nO (Optional[int]): Number of classes.
+    """
+
+    fill_defaults = {"b": 0, "W": 0}
+    reductions = []
+    if use_reduce_first:
+        reductions.append(reduce_first())
+    if use_reduce_last:
+        reductions.append(reduce_last())
+    if use_reduce_max:
+        reductions.append(reduce_max())
+    if use_reduce_mean:
+        reductions.append(reduce_mean())
+
+    if not len(reductions):
+        raise ValueError(Errors.E1057)
+
+    with Model.define_operators({">>": chain}):
+        cnn = tok2vec >> list2ragged() >> concatenate(*reductions)
+        nO_tok2vec = tok2vec.maybe_get_dim("nO")
+        nI = nO_tok2vec * len(reductions) if nO_tok2vec is not None else None
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO, nI=nI)
+            fill_defaults["b"] = NEG_VALUE
+            resizable_layer: Model = resizable(
+                output_layer,
+                resize_layer=partial(
+                    resize_linear_weighted, fill_defaults=fill_defaults
+                ),
+            )
+            model = cnn >> resizable_layer
+        else:
+            output_layer = Linear(nO=nO, nI=nI)
+            resizable_layer = resizable(
+                output_layer,
+                resize_layer=partial(
+                    resize_linear_weighted, fill_defaults=fill_defaults
+                ),
+            )
+            model = cnn >> resizable_layer >> Logistic()
+        model.set_ref("output_layer", output_layer)
+        model.attrs["resize_output"] = partial(
+            resize_and_set_ref,
+            resizable_layer=resizable_layer,
+        )
+    model.set_ref("tok2vec", tok2vec)
+    if nO is not None:
+        model.set_dim("nO", cast(int, nO))
+    model.attrs["multi_label"] = not exclusive_classes
+    return model

spacy/pipeline/_parser_internals/stateclass.pyx

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

spacy/pipeline/textcat.py

@@ -36,8 +36,9 @@ maxout_pieces = 3
 depth = 2
 
 [model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 """
@@ -45,16 +46,21 @@ DEFAULT_SINGLE_TEXTCAT_MODEL = Config().from_str(single_label_default_config)["m
 
 single_label_bow_config = """
 [model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 """
 
 single_label_cnn_config = """
 [model]
-@architectures = "spacy.TextCatCNN.v2"
+@architectures = "spacy.TextCatReduce.v1"
 exclusive_classes = true
+use_reduce_first = false
+use_reduce_last = false
+use_reduce_max = false
+use_reduce_mean = true
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"

spacy/pipeline/textcat_multilabel.py

@@ -35,8 +35,9 @@ maxout_pieces = 3
 depth = 2
 
 [model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
+length = 262144
 ngram_size = 1
 no_output_layer = false
 """
@@ -44,7 +45,7 @@ DEFAULT_MULTI_TEXTCAT_MODEL = Config().from_str(multi_label_default_config)["mod
 
 multi_label_bow_config = """
 [model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = false
 ngram_size = 1
 no_output_layer = false
@@ -52,8 +53,12 @@ no_output_layer = false
 
 multi_label_cnn_config = """
 [model]
-@architectures = "spacy.TextCatCNN.v2"
+@architectures = "spacy.TextCatReduce.v1"
 exclusive_classes = false
+use_reduce_first = false
+use_reduce_last = false
+use_reduce_max = false
+use_reduce_mean = true
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"

spacy/scorer.py

@@ -802,6 +802,140 @@ def get_ner_prf(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
         }
 
 
+# The following implementation of trapezoid() is adapted from SciPy,
+# which is distributed under the New BSD License.
+# Copyright (c) 2001-2002 Enthought, Inc. 2003-2023, SciPy Developers.
+# See licenses/3rd_party_licenses.txt
+def trapezoid(y, x=None, dx=1.0, axis=-1):
+    r"""
+    Integrate along the given axis using the composite trapezoidal rule.
+
+    If `x` is provided, the integration happens in sequence along its
+    elements - they are not sorted.
+
+    Integrate `y` (`x`) along each 1d slice on the given axis, compute
+    :math:`\int y(x) dx`.
+    When `x` is specified, this integrates along the parametric curve,
+    computing :math:`\int_t y(t) dt =
+    \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`.
+
+    Parameters
+    ----------
+    y : array_like
+        Input array to integrate.
+    x : array_like, optional
+        The sample points corresponding to the `y` values. If `x` is None,
+        the sample points are assumed to be evenly spaced `dx` apart. The
+        default is None.
+    dx : scalar, optional
+        The spacing between sample points when `x` is None. The default is 1.
+    axis : int, optional
+        The axis along which to integrate.
+
+    Returns
+    -------
+    trapezoid : float or ndarray
+        Definite integral of `y` = n-dimensional array as approximated along
+        a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
+        then the result is a float. If `n` is greater than 1, then the result
+        is an `n`-1 dimensional array.
+
+    See Also
+    --------
+    cumulative_trapezoid, simpson, romb
+
+    Notes
+    -----
+    Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
+    will be taken from `y` array, by default x-axis distances between
+    points will be 1.0, alternatively they can be provided with `x` array
+    or with `dx` scalar.  Return value will be equal to combined area under
+    the red lines.
+
+    References
+    ----------
+    .. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule
+
+    .. [2] Illustration image:
+           https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
+
+    Examples
+    --------
+    Use the trapezoidal rule on evenly spaced points:
+
+    >>> import numpy as np
+    >>> from scipy import integrate
+    >>> integrate.trapezoid([1, 2, 3])
+    4.0
+
+    The spacing between sample points can be selected by either the
+    ``x`` or ``dx`` arguments:
+
+    >>> integrate.trapezoid([1, 2, 3], x=[4, 6, 8])
+    8.0
+    >>> integrate.trapezoid([1, 2, 3], dx=2)
+    8.0
+
+    Using a decreasing ``x`` corresponds to integrating in reverse:
+
+    >>> integrate.trapezoid([1, 2, 3], x=[8, 6, 4])
+    -8.0
+
+    More generally ``x`` is used to integrate along a parametric curve. We can
+    estimate the integral :math:`\int_0^1 x^2 = 1/3` using:
+
+    >>> x = np.linspace(0, 1, num=50)
+    >>> y = x**2
+    >>> integrate.trapezoid(y, x)
+    0.33340274885464394
+
+    Or estimate the area of a circle, noting we repeat the sample which closes
+    the curve:
+
+    >>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
+    >>> integrate.trapezoid(np.cos(theta), x=np.sin(theta))
+    3.141571941375841
+
+    ``trapezoid`` can be applied along a specified axis to do multiple
+    computations in one call:
+
+    >>> a = np.arange(6).reshape(2, 3)
+    >>> a
+    array([[0, 1, 2],
+           [3, 4, 5]])
+    >>> integrate.trapezoid(a, axis=0)
+    array([1.5, 2.5, 3.5])
+    >>> integrate.trapezoid(a, axis=1)
+    array([2.,  8.])
+    """
+    y = np.asanyarray(y)
+    if x is None:
+        d = dx
+    else:
+        x = np.asanyarray(x)
+        if x.ndim == 1:
+            d = np.diff(x)
+            # reshape to correct shape
+            shape = [1] * y.ndim
+            shape[axis] = d.shape[0]
+            d = d.reshape(shape)
+        else:
+            d = np.diff(x, axis=axis)
+    nd = y.ndim
+    slice1 = [slice(None)] * nd
+    slice2 = [slice(None)] * nd
+    slice1[axis] = slice(1, None)
+    slice2[axis] = slice(None, -1)
+    try:
+        ret = (d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0).sum(axis)
+    except ValueError:
+        # Operations didn't work, cast to ndarray
+        d = np.asarray(d)
+        y = np.asarray(y)
+        ret = np.add.reduce(d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0, axis)
+    return ret
+
+
 # The following implementation of roc_auc_score() is adapted from
 # scikit-learn, which is distributed under the New BSD License.
 # Copyright (c) 2007–2019 The scikit-learn developers.
@@ -1024,9 +1158,9 @@ def _auc(x, y):
         else:
             raise ValueError(Errors.E164.format(x=x))
 
-    area = direction * np.trapz(y, x)
+    area = direction * trapezoid(y, x)
     if isinstance(area, np.memmap):
-        # Reductions such as .sum used internally in np.trapz do not return a
+        # Reductions such as .sum used internally in trapezoid do not return a
         # scalar by default for numpy.memmap instances contrary to
         # regular numpy.ndarray instances.
         area = area.dtype.type(area)

spacy/tests/conftest.py

@@ -162,6 +162,11 @@ def fi_tokenizer():
     return get_lang_class("fi")().tokenizer
 
 
+@pytest.fixture(scope="session")
+def fo_tokenizer():
+    return get_lang_class("fo")().tokenizer
+
+
 @pytest.fixture(scope="session")
 def fr_tokenizer():
     return get_lang_class("fr")().tokenizer
@@ -317,6 +322,11 @@ def nl_tokenizer():
     return get_lang_class("nl")().tokenizer
 
 
+@pytest.fixture(scope="session")
+def nn_tokenizer():
+    return get_lang_class("nn")().tokenizer
+
+
 @pytest.fixture(scope="session")
 def pl_tokenizer():
     return get_lang_class("pl")().tokenizer

spacy/tests/lang/fo/test_tokenizer.py

@@ -0,0 +1,26 @@
+import pytest
+
+# examples taken from Basic LAnguage Resource Kit 1.0 for Faroese (https://maltokni.fo/en/resources) licensed with CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
+# fmt: off
+FO_TOKEN_EXCEPTION_TESTS = [
+    (
+        "Eftir løgtingslóg um samsýning og eftirløn landsstýrismanna v.m., skulu løgmaður og landsstýrismenn vanliga siga frá sær størv í almennari tænastu ella privatum virkjum, samtøkum ella stovnum. ",
+        [
+            "Eftir", "løgtingslóg", "um", "samsýning", "og", "eftirløn", "landsstýrismanna", "v.m.", ",", "skulu", "løgmaður", "og", "landsstýrismenn", "vanliga", "siga", "frá", "sær", "størv", "í", "almennari", "tænastu", "ella", "privatum", "virkjum", ",", "samtøkum", "ella", "stovnum", ".",
+        ],
+    ),
+    (
+        "Sambandsflokkurin gongur aftur við 2,7 prosentum í mun til valið í 1994, tá flokkurin fekk undirtøku frá 23,4 prosent av veljarunum.",
+        [
+            "Sambandsflokkurin", "gongur", "aftur", "við", "2,7", "prosentum", "í", "mun", "til", "valið", "í", "1994", ",", "tá", "flokkurin", "fekk", "undirtøku", "frá", "23,4", "prosent", "av", "veljarunum", ".",
+        ],
+    ),
+]
+# fmt: on
+
+
+@pytest.mark.parametrize("text,expected_tokens", FO_TOKEN_EXCEPTION_TESTS)
+def test_fo_tokenizer_handles_exception_cases(fo_tokenizer, text, expected_tokens):
+    tokens = fo_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/lang/nn/test_tokenizer.py

@@ -0,0 +1,38 @@
+import pytest
+
+# examples taken from Omsetjingsminne frå Nynorsk pressekontor 2022 (https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-80/)
+# fmt: off
+NN_TOKEN_EXCEPTION_TESTS = [
+    (
+        "Målet til direktoratet er at alle skal bli tilbydd jobb i politiet så raskt som mogleg i 2014.",
+        [
+            "Målet", "til", "direktoratet", "er", "at", "alle", "skal", "bli", "tilbydd", "jobb", "i", "politiet", "så", "raskt", "som", "mogleg", "i", "2014", ".",
+        ],
+    ),
+    (
+        "Han ønskjer ikkje at staten skal vere med på å finansiere slik undervisning, men dette er rektor på skulen ueinig i.",
+        [
+            "Han", "ønskjer", "ikkje", "at", "staten", "skal", "vere", "med", "på", "å", "finansiere", "slik", "undervisning", ",", "men", "dette", "er", "rektor", "på", "skulen", "ueinig", "i", ".",
+        ],
+    ),
+    (
+        "Ifølgje China Daily vart det 8.848 meter høge fjellet flytta 3 centimeter sørvestover under jordskjelvet, som vart målt til 7,8.",
+        [
+            "Ifølgje", "China", "Daily", "vart", "det", "8.848", "meter", "høge", "fjellet", "flytta", "3", "centimeter", "sørvestover", "under", "jordskjelvet", ",", "som", "vart", "målt", "til", "7,8", ".",
+        ],
+    ),
+    (
+        "Brukssesongen er frå nov. til mai, med ein topp i mars.",
+        [
+            "Brukssesongen", "er", "frå", "nov.", "til", "mai", ",", "med", "ein", "topp", "i", "mars", ".",
+        ],
+    ),
+]
+# fmt: on
+
+
+@pytest.mark.parametrize("text,expected_tokens", NN_TOKEN_EXCEPTION_TESTS)
+def test_nn_tokenizer_handles_exception_cases(nn_tokenizer, text, expected_tokens):
+    tokens = nn_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/pipeline/test_pipe_factories.py

@@ -203,7 +203,7 @@ def test_pipe_class_component_model():
             "@architectures": "spacy.TextCatEnsemble.v2",
             "tok2vec": DEFAULT_TOK2VEC_MODEL,
             "linear_model": {
-                "@architectures": "spacy.TextCatBOW.v2",
+                "@architectures": "spacy.TextCatBOW.v3",
                 "exclusive_classes": False,
                 "ngram_size": 1,
                 "no_output_layer": False,

spacy/tests/pipeline/test_textcat.py

@@ -414,7 +414,7 @@ def test_implicit_label(name, get_examples):
 @pytest.mark.parametrize(
     "name,textcat_config",
     [
-        # BOW
+        # BOW V1
         ("textcat", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
         ("textcat", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
         ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
@@ -451,14 +451,14 @@ def test_no_resize(name, textcat_config):
 @pytest.mark.parametrize(
     "name,textcat_config",
     [
-        # BOW
-        ("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
-        ("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
-        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
-        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
+        # BOW V3
+        ("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
+        ("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
         # CNN
-        ("textcat", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
-        ("textcat_multilabel", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
+        ("textcat", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
     ],
 )
 # fmt: on
@@ -480,14 +480,14 @@ def test_resize(name, textcat_config):
 @pytest.mark.parametrize(
     "name,textcat_config",
     [
-        # BOW
-        ("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
-        ("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
-        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
-        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
-        # CNN
-        ("textcat", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
-        ("textcat_multilabel", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
+        # BOW v3
+        ("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
+        ("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
+        # REDUCE
+        ("textcat", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
+        ("textcat_multilabel", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
     ],
 )
 # fmt: on
@@ -693,12 +693,23 @@ def test_overfitting_IO_multi():
         ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
         ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
         ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
+        # BOW V3
+        ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}),
+        ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
+        ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
+        ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
         # ENSEMBLE V2
-        ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
-        ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
-        # CNN V2
+        ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
+        ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
+        # CNN V2 (legacy)
         ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
         ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
+        # PARAMETRIC ATTENTION V1
+        ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
+        ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
+        # REDUCE V1
+        ("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
+        ("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
     ],
 )
 # fmt: on

spacy/tests/test_cli_app.py

@@ -214,9 +214,6 @@ def test_project_clone(options):
         assert (out / "README.md").is_file()
 
 
-@pytest.mark.skipif(
-    sys.version_info >= (3, 12), reason="Python 3.12+ not supported for remotes"
-)
 def test_project_push_pull(project_dir):
     proj = dict(SAMPLE_PROJECT)
     remote = "xyz"
@@ -241,7 +238,7 @@ def test_project_push_pull(project_dir):
 
 def test_find_function_valid():
     # example of architecture in main code base
-    function = "spacy.TextCatBOW.v2"
+    function = "spacy.TextCatBOW.v3"
     result = CliRunner().invoke(app, ["find-function", function, "-r", "architectures"])
     assert f"Found registered function '{function}'" in result.stdout
     assert "textcat.py" in result.stdout
@@ -260,7 +257,7 @@ def test_find_function_valid():
 
 def test_find_function_invalid():
     # invalid registry
-    function = "spacy.TextCatBOW.v2"
+    function = "spacy.TextCatBOW.v3"
     registry = "foobar"
     result = CliRunner().invoke(
         app, ["find-function", function, "--registry", registry]

spacy/tests/test_misc.py

@@ -376,8 +376,9 @@ def test_util_dot_section():
     factory = "textcat"
 
     [components.textcat.model]
-    @architectures = "spacy.TextCatBOW.v2"
+    @architectures = "spacy.TextCatBOW.v3"
     exclusive_classes = true
+    length = 262144
     ngram_size = 1
     no_output_layer = false
     """
@@ -485,8 +486,8 @@ def test_to_ternary_int():
 
 def test_find_available_port():
     host = "0.0.0.0"
-    port = 5000
-    assert find_available_port(port, host) == port, "Port 5000 isn't free"
+    port = 5001
+    assert find_available_port(port, host) == port, "Port 5001 isn't free"
 
     from wsgiref.simple_server import demo_app, make_server
 

spacy/tests/test_models.py

@@ -26,6 +26,7 @@ from spacy.ml.models import (
     build_Tok2Vec_model,
 )
 from spacy.ml.staticvectors import StaticVectors
+from spacy.util import registry
 
 
 def get_textcat_bow_kwargs():
@@ -284,3 +285,17 @@ def test_spancat_model_forward_backward(nO=5):
     Y, backprop = model((docs, spans), is_train=True)
     assert Y.shape == (spans.dataXd.shape[0], nO)
     backprop(Y)
+
+
+def test_textcat_reduce_invalid_args():
+    textcat_reduce = registry.architectures.get("spacy.TextCatReduce.v1")
+    tok2vec = make_test_tok2vec()
+    with pytest.raises(ValueError, match=r"must be used with at least one reduction"):
+        textcat_reduce(
+            tok2vec=tok2vec,
+            exclusive_classes=False,
+            use_reduce_first=False,
+            use_reduce_last=False,
+            use_reduce_max=False,
+            use_reduce_mean=False,
+        )

spacy/tests/tokenizer/test_explain.py

@@ -85,6 +85,18 @@ def test_tokenizer_explain_special_matcher(en_vocab):
     assert tokens == explain_tokens
 
 
+def test_tokenizer_explain_special_matcher_whitespace(en_vocab):
+    rules = {":]": [{"ORTH": ":]"}]}
+    tokenizer = Tokenizer(
+        en_vocab,
+        rules=rules,
+    )
+    text = ": ]"
+    tokens = [t.text for t in tokenizer(text)]
+    explain_tokens = [t[1] for t in tokenizer.explain(text)]
+    assert tokens == explain_tokens
+
+
 @hypothesis.strategies.composite
 def sentence_strategy(draw: hypothesis.strategies.DrawFn, max_n_words: int = 4) -> str:
     """
@@ -123,6 +135,9 @@ def test_tokenizer_explain_fuzzy(lang: str, sentence: str) -> None:
     """
 
     tokenizer: Tokenizer = spacy.blank(lang).tokenizer
-    tokens = [t.text for t in tokenizer(sentence) if not t.is_space]
+    # Tokenizer.explain is not intended to handle whitespace or control
+    # characters in the same way as Tokenizer
+    sentence = re.sub(r"\s+", " ", sentence).strip()
+    tokens = [t.text for t in tokenizer(sentence)]
     debug_tokens = [t[1] for t in tokenizer.explain(sentence)]
     assert tokens == debug_tokens, f"{tokens}, {debug_tokens}, {sentence}"

spacy/tokenizer.pyx

@@ -730,9 +730,16 @@ cdef class Tokenizer:
             if i in spans_by_start:
                 span = spans_by_start[i]
                 exc = [d[ORTH] for d in special_cases[span.label_]]
-                for j, orth in enumerate(exc):
-                    final_tokens.append((f"SPECIAL-{j + 1}", self.vocab.strings[orth]))
-                i += len(span)
+                # The phrase matcher can overmatch for tokens separated by
+                # spaces in the text but not in the underlying rule, so skip
+                # cases where the texts aren't identical
+                if span.text != "".join([self.vocab.strings[orth] for orth in exc]):
+                    final_tokens.append(tokens[i])
+                    i += 1
+                else:
+                    for j, orth in enumerate(exc):
+                        final_tokens.append((f"SPECIAL-{j + 1}", self.vocab.strings[orth]))
+                    i += len(span)
             else:
                 final_tokens.append(tokens[i])
                 i += 1

spacy/tokens/doc.pyi

@@ -42,7 +42,7 @@ class Doc:
     user_hooks: Dict[str, Callable[..., Any]]
     user_token_hooks: Dict[str, Callable[..., Any]]
     user_span_hooks: Dict[str, Callable[..., Any]]
-    tensor: np.ndarray[Any, np.dtype[np.float_]]
+    tensor: np.ndarray[Any, np.dtype[np.float64]]
     user_data: Dict[str, Any]
     has_unknown_spaces: bool
     _context: Any
@@ -125,7 +125,7 @@ class Doc:
         vector: Optional[Floats1d] = ...,
         alignment_mode: str = ...,
         span_id: Union[int, str] = ...,
-    ) -> Span: ...
+    ) -> Optional[Span]: ...
     def similarity(self, other: Union[Doc, Span, Token, Lexeme]) -> float: ...
     @property
     def has_vector(self) -> bool: ...
@@ -166,7 +166,7 @@ class Doc:
     ) -> Doc: ...
     def to_array(
         self, py_attr_ids: Union[int, str, List[Union[int, str]]]
-    ) -> np.ndarray[Any, np.dtype[np.float_]]: ...
+    ) -> np.ndarray[Any, np.dtype[np.float64]]: ...
     @staticmethod
     def from_docs(
         docs: List[Doc],
@@ -179,15 +179,13 @@ class Doc:
         self, path: Union[str, Path], *, exclude: Iterable[str] = ...
     ) -> None: ...
     def from_disk(
-        self, path: Union[str, Path], *, exclude: Union[List[str], Tuple[str]] = ...
+        self, path: Union[str, Path], *, exclude: Iterable[str] = ...
     ) -> Doc: ...
-    def to_bytes(self, *, exclude: Union[List[str], Tuple[str]] = ...) -> bytes: ...
-    def from_bytes(
-        self, bytes_data: bytes, *, exclude: Union[List[str], Tuple[str]] = ...
-    ) -> Doc: ...
-    def to_dict(self, *, exclude: Union[List[str], Tuple[str]] = ...) -> bytes: ...
+    def to_bytes(self, *, exclude: Iterable[str] = ...) -> bytes: ...
+    def from_bytes(self, bytes_data: bytes, *, exclude: Iterable[str] = ...) -> Doc: ...
+    def to_dict(self, *, exclude: Iterable[str] = ...) -> Dict[str, Any]: ...
     def from_dict(
-        self, msg: bytes, *, exclude: Union[List[str], Tuple[str]] = ...
+        self, msg: Dict[str, Any], *, exclude: Iterable[str] = ...
     ) -> Doc: ...
     def extend_tensor(self, tensor: Floats2d) -> None: ...
     def retokenize(self) -> Retokenizer: ...

spacy/tokens/doc.pyx

@@ -1326,7 +1326,7 @@ cdef class Doc:
 
         path (str / Path): A path to a directory. Paths may be either
             strings or `Path`-like objects.
-        exclude (list): String names of serialization fields to exclude.
+        exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Doc): The modified `Doc` object.
 
         DOCS: https://spacy.io/api/doc#from_disk
@@ -1339,7 +1339,7 @@ cdef class Doc:
     def to_bytes(self, *, exclude=tuple()):
         """Serialize, i.e. export the document contents to a binary string.
 
-        exclude (list): String names of serialization fields to exclude.
+        exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
             all annotations.
 
@@ -1351,7 +1351,7 @@ cdef class Doc:
         """Deserialize, i.e. import the document contents from a binary string.
 
         data (bytes): The string to load from.
-        exclude (list): String names of serialization fields to exclude.
+        exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Doc): Itself.
 
         DOCS: https://spacy.io/api/doc#from_bytes
@@ -1361,11 +1361,8 @@ cdef class Doc:
     def to_dict(self, *, exclude=tuple()):
         """Export the document contents to a dictionary for serialization.
 
-        exclude (list): String names of serialization fields to exclude.
-        RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
-            all annotations.
-
-        DOCS: https://spacy.io/api/doc#to_bytes
+        exclude (Iterable[str]): String names of serialization fields to exclude.
+        RETURNS (Dict[str, Any]): A dictionary representation of the `Doc`
         """
         array_head = Doc._get_array_attrs()
         strings = set()
@@ -1411,13 +1408,11 @@ cdef class Doc:
         return util.to_dict(serializers, exclude)
 
     def from_dict(self, msg, *, exclude=tuple()):
-        """Deserialize, i.e. import the document contents from a binary string.
+        """Deserialize the document contents from a dictionary representation.
 
-        data (bytes): The string to load from.
-        exclude (list): String names of serialization fields to exclude.
+        msg (Dict[str, Any]): The dictionary to load from.
+        exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Doc): Itself.
-
-        DOCS: https://spacy.io/api/doc#from_dict
         """
         if self.length != 0:
             raise ValueError(Errors.E033.format(length=self.length))

spacy/util.py

@@ -1077,20 +1077,38 @@ def make_tempdir() -> Generator[Path, None, None]:
 
 
 def is_in_jupyter() -> bool:
-    """Check if user is running spaCy from a Jupyter notebook by detecting the
-    IPython kernel. Mainly used for the displaCy visualizer.
-    RETURNS (bool): True if in Jupyter, False if not.
+    """Check if user is running spaCy from a Jupyter or Colab notebook by
+    detecting the IPython kernel. Mainly used for the displaCy visualizer.
+    RETURNS (bool): True if in Jupyter/Colab, False if not.
     """
     # https://stackoverflow.com/a/39662359/6400719
+    # https://stackoverflow.com/questions/15411967
     try:
-        shell = get_ipython().__class__.__name__  # type: ignore[name-defined]
-        if shell == "ZMQInteractiveShell":
+        if get_ipython().__class__.__name__ == "ZMQInteractiveShell":  # type: ignore[name-defined]
             return True  # Jupyter notebook or qtconsole
+        if get_ipython().__class__.__module__ == "google.colab._shell":  # type: ignore[name-defined]
+            return True  # Colab notebook
     except NameError:
-        return False  # Probably standard Python interpreter
+        pass  # Probably standard Python interpreter
+    # additional check for Colab
+    try:
+        import google.colab
+
+        return True  # Colab notebook
+    except ImportError:
+        pass
     return False
 
 
+def is_in_interactive() -> bool:
+    """Check if user is running spaCy from an interactive Python
+    shell. Will return True in Jupyter notebooks too.
+    RETURNS (bool): True if in interactive mode, False if not.
+    """
+    # https://stackoverflow.com/questions/2356399/tell-if-python-is-in-interactive-mode
+    return hasattr(sys, "ps1") or hasattr(sys, "ps2")
+
+
 def get_object_name(obj: Any) -> str:
     """Get a human-readable name of a Python object, e.g. a pipeline component.
 

website/docs/api/architectures.mdx

@@ -78,16 +78,16 @@ subword features, and a
 [MaxoutWindowEncoder](/api/architectures#MaxoutWindowEncoder) encoding layer
 consisting of a CNN and a layer-normalized maxout activation function.
 
-| Name                 | Description                                                                                                                                                                                                                                                                   |
-| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `width`              | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~                                                                                                          |
-| `depth`              | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~                                                                                                                                                                                |
-| `embed_size`         | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~                                                                                            |
+| Name                 | Description                                                                                                                                                                                                                                                                 |
+| -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `width`              | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~                                                                                                        |
+| `depth`              | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~                                                                                                                                                                              |
+| `embed_size`         | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~                                                                                          |
 | `window_size`        | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * window_size * 2 + 1`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. ~~int~~ |
-| `maxout_pieces`      | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~                                                                                   |
-| `subword_features`   | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~                                       |
-| `pretrained_vectors` | Whether to also use static vectors. ~~bool~~                                                                                                                                                                                                                                  |
-| **CREATES**          | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                                                        |
+| `maxout_pieces`      | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~                                                                                 |
+| `subword_features`   | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~                                     |
+| `pretrained_vectors` | Whether to also use static vectors. ~~bool~~                                                                                                                                                                                                                                |
+| **CREATES**          | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                                                      |
 
 ### spacy.Tok2VecListener.v1 {id="Tok2VecListener"}
 
@@ -962,8 +962,9 @@ single-label use-cases where `exclusive_classes = true`, while the
 > nO = null
 >
 > [model.linear_model]
-> @architectures = "spacy.TextCatBOW.v2"
+> @architectures = "spacy.TextCatBOW.v3"
 > exclusive_classes = true
+> length = 262144
 > ngram_size = 1
 > no_output_layer = false
 >
@@ -1017,54 +1018,15 @@ but used an internal `tok2vec` instead of taking it as argument:
 
 </Accordion>
 
-### spacy.TextCatCNN.v2 {id="TextCatCNN"}
+### spacy.TextCatBOW.v3 {id="TextCatBOW"}
 
 > #### Example Config
 >
 > ```ini
 > [model]
-> @architectures = "spacy.TextCatCNN.v2"
-> exclusive_classes = false
-> nO = null
->
-> [model.tok2vec]
-> @architectures = "spacy.HashEmbedCNN.v2"
-> pretrained_vectors = null
-> width = 96
-> depth = 4
-> embed_size = 2000
-> window_size = 1
-> maxout_pieces = 3
-> subword_features = true
-> ```
-
-A neural network model where token vectors are calculated using a CNN. The
-vectors are mean pooled and used as features in a feed-forward network. This
-architecture is usually less accurate than the ensemble, but runs faster.
-
-| Name                | Description                                                                                                                                                                                    |
-| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~                                                                                                                                     |
-| `tok2vec`           | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~                                                                                                                                        |
-| `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
-| **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
-
-<Accordion title="spacy.TextCatCNN.v1 definition" spaced>
-
-[TextCatCNN.v1](/api/legacy#TextCatCNN_v1) had the exact same signature, but was
-not yet resizable. Since v2, new labels can be added to this component, even
-after training.
-
-</Accordion>
-
-### spacy.TextCatBOW.v2 {id="TextCatBOW"}
-
-> #### Example Config
->
-> ```ini
-> [model]
-> @architectures = "spacy.TextCatBOW.v2"
+> @architectures = "spacy.TextCatBOW.v3"
 > exclusive_classes = false
+> length = 262144
 > ngram_size = 1
 > no_output_layer = false
 > nO = null
@@ -1078,17 +1040,108 @@ the others, but may not be as accurate, especially if texts are short.
 | `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~                                                                                                                                     |
 | `ngram_size`        | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3` would give unigram, trigram and bigram features. ~~int~~                                           |
 | `no_output_layer`   | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`). ~~bool~~                                                          |
+| `length`            | The size of the weights vector. The length will be rounded up to the next power of two if it is not a power of two. Defaults to `262144`. ~~int~~                                              |
 | `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
 | **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
 
-<Accordion title="spacy.TextCatBOW.v1 definition" spaced>
+<Accordion title="Previous versions of spacy.TextCatBOW" spaced>
 
-[TextCatBOW.v1](/api/legacy#TextCatBOW_v1) had the exact same signature, but was
-not yet resizable. Since v2, new labels can be added to this component, even
-after training.
+- [TextCatBOW.v1](/api/legacy#TextCatBOW_v1) was not yet resizable. Since v2,
+  new labels can be added to this component, even after training.
+- [TextCatBOW.v1](/api/legacy#TextCatBOW_v1) and
+  [TextCatBOW.v2](/api/legacy#TextCatBOW_v2) used an erroneous sparse linear
+  layer that only used a small number of the allocated parameters.
+- [TextCatBOW.v1](/api/legacy#TextCatBOW_v1) and
+  [TextCatBOW.v2](/api/legacy#TextCatBOW_v2) did not have the `length` argument.
 
 </Accordion>
 
+### spacy.TextCatParametricAttention.v1 {id="TextCatParametricAttention"}
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.TextCatParametricAttention.v1"
+> exclusive_classes = true
+> nO = null
+>
+> [model.tok2vec]
+> @architectures = "spacy.Tok2Vec.v2"
+>
+> [model.tok2vec.embed]
+> @architectures = "spacy.MultiHashEmbed.v2"
+> width = 64
+> rows = [2000, 2000, 1000, 1000, 1000, 1000]
+> attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
+> include_static_vectors = false
+>
+> [model.tok2vec.encode]
+> @architectures = "spacy.MaxoutWindowEncoder.v2"
+> width = ${model.tok2vec.embed.width}
+> window_size = 1
+> maxout_pieces = 3
+> depth = 2
+> ```
+
+A neural network model that is built upon Tok2Vec and uses parametric attention
+to attend to tokens that are relevant to text classification.
+
+| Name                | Description                                                                                                                                                                                    |
+| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `tok2vec`           | The `tok2vec` layer to build the neural network upon. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                     |
+| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~                                                                                                                                     |
+| `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
+| **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
+
+### spacy.TextCatReduce.v1 {id="TextCatReduce"}
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.TextCatReduce.v1"
+> exclusive_classes = false
+> use_reduce_first = false
+> use_reduce_last = false
+> use_reduce_max = false
+> use_reduce_mean = true
+> nO = null
+>
+> [model.tok2vec]
+> @architectures = "spacy.HashEmbedCNN.v2"
+> pretrained_vectors = null
+> width = 96
+> depth = 4
+> embed_size = 2000
+> window_size = 1
+> maxout_pieces = 3
+> subword_features = true
+> ```
+
+A classifier that pools token hidden representations of each `Doc` using first,
+max or mean reduction and then applies a classification layer. Reductions are
+concatenated when multiple reductions are used.
+
+<Infobox variant="warning" title="Relation to TextCatCNN" id="TextCatCNN">
+
+`TextCatReduce` is a generalization of the older
+[`TextCatCNN`](/api/legacy#TextCatCNN_v2) model. `TextCatCNN` always uses a mean
+reduction, whereas `TextCatReduce` also supports first/max reductions.
+
+</Infobox>
+
+| Name                | Description                                                                                                                                                                                    |
+| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~                                                                                                                                     |
+| `tok2vec`           | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~                                                                                                                                        |
+| `use_reduce_first`  | Pool by using the hidden representation of the first token of a `Doc`. ~~bool~~                                                                                                                |
+| `use_reduce_last`   | Pool by using the hidden representation of the last token of a `Doc`. ~~bool~~                                                                                                                 |
+| `use_reduce_max`    | Pool by taking the maximum values of the hidden representations of a `Doc`. ~~bool~~                                                                                                           |
+| `use_reduce_mean`   | Pool by taking the mean of all hidden representations of a `Doc`. ~~bool~~                                                                                                                     |
+| `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
+| **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
+
 ## Span classification architectures {id="spancat",source="spacy/ml/models/spancat.py"}
 
 ### spacy.SpanCategorizer.v1 {id="SpanCategorizer"}

website/docs/api/curatedtransformer.mdx

@@ -400,6 +400,14 @@ identifiers are grouped by token. Instances of this class are typically assigned
 to the [`Doc._.trf_data`](/api/curatedtransformer#assigned-attributes) extension
 attribute.
 
+> #### Example
+>
+> ```python
+> # Get the last hidden layer output for "is" (token index 1)
+> doc = nlp("This is a text.")
+> tensors = doc._.trf_data.last_hidden_layer_state[1]
+> ```
+
 | Name              | Description                                                                                                                                                                        |
 | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `all_outputs`     | List of `Ragged` tensors that correspends to outputs of the different transformer layers. Each tensor element corresponds to a piece identifier's representation. ~~List[Ragged]~~ |

website/docs/api/large-language-models.mdx

@@ -9,13 +9,21 @@ menu:
   - ['Various Functions', 'various-functions']
 ---
 
-[The spacy-llm package](https://github.com/explosion/spacy-llm) integrates Large
-Language Models (LLMs) into spaCy, featuring a modular system for **fast
+[The `spacy-llm` package](https://github.com/explosion/spacy-llm) integrates
+Large Language Models (LLMs) into spaCy, featuring a modular system for **fast
 prototyping** and **prompting**, and turning unstructured responses into
 **robust outputs** for various NLP tasks, **no training data** required.
 
 ## Config and implementation {id="config"}
 
+An LLM component is implemented through the `LLMWrapper` class. It is accessible
+through a generic `llm`
+[component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories)
+as well as through task-specific component factories: `llm_ner`, `llm_spancat`,
+`llm_rel`, `llm_textcat`, `llm_sentiment`, `llm_summarization`,
+`llm_entity_linker`, `llm_raw` and `llm_translation`. For these factories, the
+GPT-3-5 model from OpenAI is used by default, but this can be customized.
+
 > #### Example
 >
 > ```python
@@ -34,14 +42,6 @@ prototyping** and **prompting**, and turning unstructured responses into
 > llm = LLMWrapper(vocab=nlp.vocab, task=task, model=model, cache=cache, save_io=True)
 > ```
 
-An LLM component is implemented through the `LLMWrapper` class. It is accessible
-through a generic `llm`
-[component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories)
-as well as through task-specific component factories: `llm_ner`, `llm_spancat`,
-`llm_rel`, `llm_textcat`, `llm_sentiment` and `llm_summarization`. For these
-factories, the GPT-3-5 model from OpenAI is used by default, but this can be
-customized.
-
 ### LLMWrapper.\_\_init\_\_ {id="init",tag="method"}
 
 Create a new pipeline instance. In your application, you would normally use a
@@ -206,13 +206,82 @@ not require labels.
 
 ## Tasks {id="tasks"}
 
-### Task implementation {id="task-implementation"}
+In `spacy-llm`, a _task_ defines an NLP problem or question and its solution
+using an LLM. It does so by implementing the following responsibilities:
 
-A _task_ defines an NLP problem or question, that will be sent to the LLM via a
-prompt. Further, the task defines how to parse the LLM's responses back into
-structured information. All tasks are registered in the `llm_tasks` registry.
+1. Loading a prompt template and injecting documents' data into the prompt.
+   Optionally, include fewshot examples in the prompt.
+2. Splitting the prompt into several pieces following a map-reduce paradigm,
+   _if_ the prompt is too long to fit into the model's context and the task
+   supports sharding prompts.
+3. Parsing the LLM's responses back into structured information and validating
+   the parsed output.
 
-#### task.generate_prompts {id="task-generate-prompts"}
+Two different task interfaces are supported: `ShardingLLMTask` and
+`NonShardingLLMTask`. Only the former supports the sharding of documents, i. e.
+splitting up prompts if they are too long.
+
+All tasks are registered in the `llm_tasks` registry.
+
+### On Sharding {id="task-sharding"}
+
+"Sharding" describes, generally speaking, the process of distributing parts of a
+dataset across multiple storage units for easier processing and lookups. In
+`spacy-llm` we use this term (synonymously: "mapping") to describe the splitting
+up of prompts if they are too long for a model to handle, and "fusing"
+(synonymously: "reducing") to describe how the model responses for several
+shards are merged back together into a single document.
+
+Prompts are broken up in a manner that _always_ keeps the prompt in the template
+intact, meaning that the instructions to the LLM will always stay complete. The
+document content however will be split, if the length of the fully rendered
+prompt exceeds a model context length.
+
+A toy example: let's assume a model has a context window of 25 tokens and the
+prompt template for our fictional, sharding-supporting task looks like this:
+
+```
+Estimate the sentiment of this text:
+"{text}"
+Estimated sentiment:
+```
+
+Depending on how tokens are counted exactly (this is a config setting), we might
+come up with `n = 12` tokens for the number of tokens in the prompt
+instructions. Furthermore let's assume that our `text` is "This has been
+amazing - I can't remember the last time I left the cinema so impressed." -
+which has roughly 19 tokens.
+
+Considering we only have 13 tokens to add to our prompt before we hit the
+context limit, we'll have to split our prompt into two parts. Thus `spacy-llm`,
+assuming the task used supports sharding, will split the prompt into two (the
+default splitting strategy splits by tokens, but alternative splitting
+strategies splitting e. g. by sentences can be configured):
+
+_(Prompt 1/2)_
+
+```
+Estimate the sentiment of this text:
+"This has been amazing - I can't remember "
+Estimated sentiment:
+```
+
+_(Prompt 2/2)_
+
+```
+Estimate the sentiment of this text:
+"the last time I left the cinema so impressed."
+Estimated sentiment:
+```
+
+The reduction step is task-specific - a sentiment estimation task might e. g. do
+a weighted average of the sentiment scores. Note that prompt sharding introduces
+potential inaccuracies, as the LLM won't have access to the entire document at
+once. Depending on your use case this might or might not be problematic.
+
+### `NonShardingLLMTask` {id="task-nonsharding"}
+
+#### task.generate_prompts {id="task-nonsharding-generate-prompts"}
 
 Takes a collection of documents, and returns a collection of "prompts", which
 can be of type `Any`. Often, prompts are of type `str` - but this is not
@@ -223,7 +292,7 @@ enforced to allow for maximum flexibility in the framework.
 | `docs`      | The input documents. ~~Iterable[Doc]~~   |
 | **RETURNS** | The generated prompts. ~~Iterable[Any]~~ |
 
-#### task.parse_responses {id="task-parse-responses"}
+#### task.parse_responses {id="task-non-sharding-parse-responses"}
 
 Takes a collection of LLM responses and the original documents, parses the
 responses into structured information, and sets the annotations on the
@@ -234,11 +303,157 @@ defined fields.
 The `responses` are of type `Iterable[Any]`, though they will often be `str`
 objects. This depends on the return type of the [model](#models).
 
-| Argument    | Description                                |
-| ----------- | ------------------------------------------ |
-| `docs`      | The input documents. ~~Iterable[Doc]~~     |
-| `responses` | The generated prompts. ~~Iterable[Any]~~   |
-| **RETURNS** | The annotated documents. ~~Iterable[Doc]~~ |
+| Argument    | Description                                            |
+| ----------- | ------------------------------------------------------ |
+| `docs`      | The input documents. ~~Iterable[Doc]~~                 |
+| `responses` | The responses received from the LLM. ~~Iterable[Any]~~ |
+| **RETURNS** | The annotated documents. ~~Iterable[Doc]~~             |
+
+### `ShardingLLMTask` {id="task-sharding"}
+
+#### task.generate_prompts {id="task-sharding-generate-prompts"}
+
+Takes a collection of documents, breaks them up into shards if necessary to fit
+all content into the model's context, and returns a collection of collections of
+"prompts" (i. e. each doc can have multiple shards, each of which have exactly
+one prompt), which can be of type `Any`. Often, prompts are of type `str` - but
+this is not enforced to allow for maximum flexibility in the framework.
+
+| Argument    | Description                                        |
+| ----------- | -------------------------------------------------- |
+| `docs`      | The input documents. ~~Iterable[Doc]~~             |
+| **RETURNS** | The generated prompts. ~~Iterable[Iterable[Any]]~~ |
+
+#### task.parse_responses {id="task-sharding-parse-responses"}
+
+Receives a collection of collections of LLM responses (i. e. each doc can have
+multiple shards, each of which have exactly one prompt / prompt response) and
+the original shards, parses the responses into structured information, sets the
+annotations on the shards, and merges back doc shards into single docs. The
+`parse_responses` function is free to set the annotations in any way, including
+`Doc` fields like `ents`, `spans` or `cats`, or using custom defined fields.
+
+The `responses` are of type `Iterable[Iterable[Any]]`, though they will often be
+`str` objects. This depends on the return type of the [model](#models).
+
+| Argument    | Description                                                      |
+| ----------- | ---------------------------------------------------------------- |
+| `shards`    | The input document shards. ~~Iterable[Iterable[Doc]]~~           |
+| `responses` | The responses received from the LLM. ~~Iterable[Iterable[Any]]~~ |
+| **RETURNS** | The annotated documents. ~~Iterable[Doc]~~                       |
+
+### Translation {id="translation"}
+
+The translation task translates texts from a defined or inferred source to a
+defined target language.
+
+#### spacy.Translation.v1 {id="translation-v1"}
+
+`spacy.Translation.v1` supports both zero-shot and few-shot prompting.
+
+> #### Example config
+>
+> ```ini
+> [components.llm.task]
+> @llm_tasks = "spacy.Translation.v1"
+> examples = null
+> target_lang = "Spanish"
+> ```
+
+| Argument                    | Description                                                                                                                                                                               |
+| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `template`                  | Custom prompt template to send to LLM model. Defaults to [translation.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/translation.v1.jinja). ~~str~~ |
+| `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                            |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[TranslationTask]]~~                                |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `TranslationExample`. ~~Optional[Type[FewshotExample]]~~                                                                                    |
+| `source_lang`               | Language to translate from. Doesn't have to be set. ~~Optional[str]~~                                                                                                                     |
+| `target_lang`               | Language to translate to. No default value, has to be set. ~~str~~                                                                                                                        |
+| `field`                     | Name of extension attribute to store translation in (i. e. the translation will be available in `doc._.{field}`). Defaults to `translation`. ~~str~~                                      |
+
+To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
+you can write down a few examples in a separate file, and provide these to be
+injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
+supports `.yml`, `.yaml`, `.json` and `.jsonl`.
+
+```yaml
+- text: 'Top of the morning to you!'
+  translation: '¡Muy buenos días!'
+- text: 'The weather is great today.'
+  translation: 'El clima está fantástico hoy.'
+- text: 'Do you know what will happen tomorrow?'
+  translation: '¿Sabes qué pasará mañana?'
+```
+
+```ini
+[components.llm.task]
+@llm_tasks = "spacy.Translation.v1"
+target_lang = "Spanish"
+[components.llm.task.examples]
+@misc = "spacy.FewShotReader.v1"
+path = "translation_examples.yml"
+```
+
+### Raw prompting {id="raw"}
+
+Different to all other tasks `spacy.Raw.vX` doesn't provide a specific prompt,
+wrapping doc data, to the model. Instead it instructs the model to reply to the
+doc content. This is handy for use cases like question answering (where each doc
+contains one question) or if you want to include customized prompts for each
+doc.
+
+#### spacy.Raw.v1 {id="raw-v1"}
+
+Note that since this task may request arbitrary information, it doesn't do any
+parsing per se - the model response is stored in a custom `Doc` attribute (i. e.
+can be accessed via `doc._.{field}`).
+
+It supports both zero-shot and few-shot prompting.
+
+> #### Example config
+>
+> ```ini
+> [components.llm.task]
+> @llm_tasks = "spacy.Raw.v1"
+> examples = null
+> ```
+
+| Argument              | Description                                                                                                                                                               |
+| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `template`            | Custom prompt template to send to LLM model. Defaults to [raw.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/raw.v1.jinja). ~~str~~ |
+| `examples`            | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                            |
+| `parse_responses`     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[RawTask]]~~                        |
+| `prompt_example_type` | Type to use for fewshot examples. Defaults to `RawExample`. ~~Optional[Type[FewshotExample]]~~                                                                            |
+| `field`               | Name of extension attribute to store model reply in (i. e. the reply will be available in `doc._.{field}`). Defaults to `reply`. ~~str~~                                  |
+
+To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
+you can write down a few examples in a separate file, and provide these to be
+injected into the prompt to the LLM. The default reader `spacy.FewShotReader.v1`
+supports `.yml`, `.yaml`, `.json` and `.jsonl`.
+
+```yaml
+# Each example can follow an arbitrary pattern. It might help the prompt performance though if the examples resemble
+# the actual docs' content.
+- text: "3 + 5 = x. What's x?"
+  reply: '8'
+
+- text: 'Write me a limerick.'
+  reply:
+    "There was an Old Man with a beard, Who said, 'It is just as I feared! Two
+    Owls and a Hen, Four Larks and a Wren, Have all built their nests in my
+    beard!"
+
+- text: "Analyse the sentiment of the text 'This is great'."
+  reply: "'This is great' expresses a very positive sentiment."
+```
+
+```ini
+[components.llm.task]
+@llm_tasks = "spacy.Raw.v1"
+field = "llm_reply"
+[components.llm.task.examples]
+@misc = "spacy.FewShotReader.v1"
+path = "raw_examples.yml"
+```
 
 ### Summarization {id="summarization"}
 
@@ -307,6 +522,171 @@ max_n_words = 20
 path = "summarization_examples.yml"
 ```
 
+### EL (Entity Linking) {id="nel"}
+
+The EL links recognized entities (see [NER](#ner)) to those in a knowledge base
+(KB). The EL task prompts the LLM to select the most likely candidate from the
+KB, whose structure can be arbitrary.
+
+Note that the documents processed by the entity linking task are expected to
+have recognized entities in their `.ents` attribute. This can be achieved by
+either running the [NER task](#ner), using a trained spaCy NER model or setting
+the entities manually prior to running the EL task.
+
+In order to be able to pull data from the KB, an object implementing the
+`CandidateSelector` protocol has to be provided. This requires two functions:
+(1) `__call__()` to fetch candidate entities for entity mentions in the text
+(assumed to be available in `Doc.ents`) and (2) `get_entity_description()` to
+fetch descriptions for any given entity ID. Descriptions can be empty, but
+ideally provide more context for entities stored in the KB.
+
+`spacy-llm` provides a `CandidateSelector` implementation
+(`spacy.CandidateSelector.v1`) that leverages a spaCy knowledge base - as used
+in an `entity_linking` component - to select candidates. This knowledge base can
+be loaded from an existing spaCy pipeline (note that the pipeline's EL component
+doesn't have to be trained) or from a separate .yaml file.
+
+#### spacy.EntityLinker.v1 {id="el-v1"}
+
+Supports zero- and few-shot prompting. Relies on a configurable component
+suggesting viable entities before letting the LLM pick the most likely
+candidate.
+
+> #### Example config for spacy.EntityLinker.v1
+>
+> ```ini
+> [paths]
+> el_nlp = null
+>
+> ...
+>
+> [components.llm.task]
+> @llm_tasks = "spacy.EntityLinker.v1"
+>
+> [initialize]
+> [initialize.components]
+> [initialize.components.llm]
+> [initialize.components.llm.candidate_selector]
+> @llm_misc = "spacy.CandidateSelector.v1"
+>
+> # Load a KB from a KB file. For loading KBs from spaCy pipelines see spacy.KBObjectLoader.v1.
+> [initialize.components.llm.candidate_selector.kb_loader]
+> @llm_misc = "spacy.KBFileLoader.v1"
+> # Path to knowledge base .yaml file.
+> path = ${paths.el_kb}
+> ```
+
+| Argument              | Description                                                                                                                                                                                   |
+| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `template`            | Custom prompt template to send to LLM model. Defaults to [entity_linker.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/entity_linker.v1.jinja). ~~str~~ |
+| `parse_responses`     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[EntityLinkerTask]]~~                                   |
+| `prompt_example_type` | Type to use for fewshot examples. Defaults to `ELExample`. ~~Optional[Type[FewshotExample]]~~                                                                                                 |
+| `examples`            | Optional callable that reads a file containing task examples for few-shot learning. If `None` is passed, zero-shot learning will be used. Defaults to `None`. ~~ExamplesConfigType~~          |
+| `scorer`              | Scorer function. Defaults to the metric used by spaCy to evaluate entity linking performance. ~~Optional[Scorer]~~                                                                            |
+
+##### spacy.CandidateSelector.v1 {id="candidate-selector-v1"}
+
+`spacy.CandidateSelector.v1` is an implementation of the `CandidateSelector`
+protocol required by [`spacy.EntityLinker.v1`](#el-v1). The built-in candidate
+selector method allows loading existing knowledge bases in several ways, e. g.
+loading from a spaCy pipeline with a (not necessarily trained) entity linking
+component, and loading from a file describing the knowlege base as a .yaml file.
+Either way the loaded data will be converted to a spaCy `InMemoryLookupKB`
+instance. The KB's selection capabilities are used to select the most likely
+entity candidates for the specified mentions.
+
+> #### Example config for spacy.CandidateSelector.v1
+>
+> ```ini
+> [initialize]
+> [initialize.components]
+> [initialize.components.llm]
+> [initialize.components.llm.candidate_selector]
+> @llm_misc = "spacy.CandidateSelector.v1"
+>
+> # Load a KB from a KB file. For loading KBs from spaCy pipelines see spacy.KBObjectLoader.v1.
+> [initialize.components.llm.candidate_selector.kb_loader]
+> @llm_misc = "spacy.KBFileLoader.v1"
+> # Path to knowledge base .yaml file.
+> path = ${paths.el_kb}
+> ```
+
+| Argument    | Description                                                       |
+| ----------- | ----------------------------------------------------------------- |
+| `kb_loader` | KB loader object. ~~InMemoryLookupKBLoader~~                      |
+| `top_n`     | Top-n candidates to include in the prompt. Defaults to 5. ~~int~~ |
+
+##### spacy.KBObjectLoader.v1 {id="kb-object-loader-v1"}
+
+Adheres to the `InMemoryLookupKBLoader` interface required by
+[`spacy.CandidateSelector.v1`](#candidate-selector-v1). Loads a knowledge base
+from an existing spaCy pipeline.
+
+> #### Example config for spacy.KBObjectLoader.v1
+>
+> ```ini
+> [initialize.components.llm.candidate_selector.kb_loader]
+> @llm_misc = "spacy.KBObjectLoader.v1"
+> # Path to knowledge base directory in serialized spaCy pipeline.
+> path = ${paths.el_kb}
+> # Path to spaCy pipeline. If this is not specified, spacy-llm tries to determine this automatically (but may fail).
+> nlp_path = ${paths.el_nlp}
+> # Path to file with descriptions for entity.
+> desc_path = ${paths.el_desc}
+> ```
+
+| Argument          | Description                                                                                                                                                                                                                         |
+| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `path`            | Path to KB file. ~~Union[str, Path]~~                                                                                                                                                                                               |
+| `nlp_path`        | Path to serialized NLP pipeline. If None, path will be guessed. ~~Optional[Union[Path, str]]~~                                                                                                                                      |
+| `desc_path`       | Path to file with descriptions for entities. ~~int~~                                                                                                                                                                                |
+| `ent_desc_reader` | Entity description reader. Defaults to an internal method expecting a CSV file without header row, with ";" as delimiters, and with two columns - one for the entitys' IDs, one for their descriptions. ~~Optional[EntDescReader]~~ |
+
+##### spacy.KBFileLoader.v1 {id="kb-file-loader-v1"}
+
+Adheres to the `InMemoryLookupKBLoader` interface required by
+[`spacy.CandidateSelector.v1`](#candidate-selector-v1). Loads a knowledge base
+from a knowledge base file. The KB .yaml file has to stick to the following
+format:
+
+```yaml
+entities:
+  # The key should be whatever ID identifies this entity uniquely in your knowledge base.
+  ID1:
+      name: "..."
+      desc: "..."
+  ID2:
+      ...
+# Data on aliases in your knowledge base - e. g. "Apple" for the entity "Apple Inc.".
+aliases:
+  - alias: "..."
+    # List of all entities that this alias refers to.
+    entities: ["ID1", "ID2", ...]
+    # Optional: prior probabilities that this alias refers to the n-th entity in the "entities" attribute.
+    probabilities: [0.5, 0.2, ...]
+  - alias: "..."
+    entities: [...]
+    probabilities: [...]
+  ...
+```
+
+See
+[here](https://github.com/explosion/spacy-llm/blob/main/usage_examples/el_openai/el_kb_data.yml)
+for a toy example of how such a KB file might look like.
+
+> #### Example config for spacy.KBFileLoader.v1
+>
+> ```ini
+> [initialize.components.llm.candidate_selector.kb_loader]
+> @llm_misc = "spacy.KBFileLoader.v1"
+> # Path to knowledge base file.
+> path = ${paths.el_kb}
+> ```
+
+| Argument | Description                           |
+| -------- | ------------------------------------- |
+| `path`   | Path to KB file. ~~Union[str, Path]~~ |
+
 ### NER {id="ner"}
 
 The NER task identifies non-overlapping entities in text.
@@ -984,9 +1364,15 @@ A _model_ defines which LLM model to query, and how to query it. It can be a
 simple function taking a collection of prompts (consistent with the output type
 of `task.generate_prompts()`) and returning a collection of responses
 (consistent with the expected input of `parse_responses`). Generally speaking,
-it's a function of type `Callable[[Iterable[Any]], Iterable[Any]]`, but specific
+it's a function of type
+`Callable[[Iterable[Iterable[Any]]], Iterable[Iterable[Any]]]`, but specific
 implementations can have other signatures, like
-`Callable[[Iterable[str]], Iterable[str]]`.
+`Callable[[Iterable[Iterable[str]]], Iterable[Iterable[str]]]`.
+
+Note: the model signature expects a nested iterable so it's able to deal with
+sharded docs. Unsharded docs (i. e. those produced by (nonsharding
+tasks)[/api/large-language-models#task-nonsharding]) are reshaped to fit the
+expected data structure.
 
 ### Models via REST API {id="models-rest"}
 
@@ -994,14 +1380,15 @@ These models all take the same parameters, but note that the `config` should
 contain provider-specific keys and values, as it will be passed onwards to the
 provider's API.
 
-| Argument           | Description                                                                                                                                       |
-| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `name`             | Model name, i. e. any supported variant for this particular model. Default depends on the specific model (cf. below) ~~str~~                      |
-| `config`           | Further configuration passed on to the model. Default depends on the specific model (cf. below). ~~Dict[Any, Any]~~                               |
-| `strict`           | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~ |
-| `max_tries`        | Max. number of tries for API request. Defaults to `5`. ~~int~~                                                                                    |
-| `max_request_time` | Max. time (in seconds) to wait for request to terminate before raising an exception. Defaults to `30.0`. ~~float~~                                |
-| `interval`         | Time interval (in seconds) for API retries in seconds. Defaults to `1.0`. ~~float~~                                                               |
+| Argument           | Description                                                                                                                                                                    |
+| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `name`             | Model name, i. e. any supported variant for this particular model. Default depends on the specific model (cf. below) ~~str~~                                                   |
+| `config`           | Further configuration passed on to the model. Default depends on the specific model (cf. below). ~~Dict[Any, Any]~~                                                            |
+| `strict`           | If `True`, raises an error if the LLM API returns a malformed response. Otherwise, return the error responses as is. Defaults to `True`. ~~bool~~                              |
+| `max_tries`        | Max. number of tries for API request. Defaults to `5`. ~~int~~                                                                                                                 |
+| `max_request_time` | Max. time (in seconds) to wait for request to terminate before raising an exception. Defaults to `30.0`. ~~float~~                                                             |
+| `interval`         | Time interval (in seconds) for API retries in seconds. Defaults to `1.0`. ~~float~~                                                                                            |
+| `endpoint`         | Endpoint URL. Defaults to the provider's standard URL, if available (which is not the case for providers with exclusively custom deployments, such as Azure) ~~Optional[str]~~ |
 
 > #### Example config:
 >
@@ -1018,8 +1405,10 @@ Currently, these models are provided as part of the core library:
 | ----------------------------- | ----------------- | ------------------------------------------------------------------------------------------------------------------ | ---------------------- | ------------------------------------ |
 | `spacy.GPT-4.v1`              | OpenAI            | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]`                                                           | `"gpt-4"`              | `{}`                                 |
 | `spacy.GPT-4.v2`              | OpenAI            | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]`                                                           | `"gpt-4"`              | `{temperature=0.0}`                  |
+| `spacy.GPT-4.v3`              | OpenAI            | All names of [GPT-4 models](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo) offered by OpenAI       | `"gpt-4"`              | `{temperature=0.0}`                  |
 | `spacy.GPT-3-5.v1`            | OpenAI            | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k", "gpt-3.5-turbo-instruct"]` | `"gpt-3.5-turbo"`      | `{}`                                 |
 | `spacy.GPT-3-5.v2`            | OpenAI            | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k", "gpt-3.5-turbo-instruct"]` | `"gpt-3.5-turbo"`      | `{temperature=0.0}`                  |
+| `spacy.GPT-3-5.v3`            | OpenAI            | All names of [GPT-3.5 models](https://platform.openai.com/docs/models/gpt-3-5) offered by OpenAI                   | `"gpt-3.5-turbo"`      | `{temperature=0.0}`                  |
 | `spacy.Davinci.v1`            | OpenAI            | `["davinci"]`                                                                                                      | `"davinci"`            | `{}`                                 |
 | `spacy.Davinci.v2`            | OpenAI            | `["davinci"]`                                                                                                      | `"davinci"`            | `{temperature=0.0, max_tokens=500}`  |
 | `spacy.Text-Davinci.v1`       | OpenAI            | `["text-davinci-003", "text-davinci-002"]`                                                                         | `"text-davinci-003"`   | `{}`                                 |
@@ -1040,6 +1429,7 @@ Currently, these models are provided as part of the core library:
 | `spacy.Text-Ada.v2`           | OpenAI            | `["text-ada-001"]`                                                                                                 | `"text-ada-001"`       | `{temperature=0.0, max_tokens=500}`  |
 | `spacy.Azure.v1`              | Microsoft, OpenAI | Arbitrary values                                                                                                   | No default             | `{temperature=0.0}`                  |
 | `spacy.Command.v1`            | Cohere            | `["command", "command-light", "command-light-nightly", "command-nightly"]`                                         | `"command"`            | `{}`                                 |
+| `spacy.Claude-2-1.v1`         | Anthropic         | `["claude-2-1"]`                                                                                                   | `"claude-2-1"`         | `{}`                                 |
 | `spacy.Claude-2.v1`           | Anthropic         | `["claude-2", "claude-2-100k"]`                                                                                    | `"claude-2"`           | `{}`                                 |
 | `spacy.Claude-1.v1`           | Anthropic         | `["claude-1", "claude-1-100k"]`                                                                                    | `"claude-1"`           | `{}`                                 |
 | `spacy.Claude-1-0.v1`         | Anthropic         | `["claude-1.0"]`                                                                                                   | `"claude-1.0"`         | `{}`                                 |
@@ -1117,7 +1507,7 @@ These models all take the same parameters:
 > ```ini
 > [components.llm.model]
 > @llm_models = "spacy.Llama2.v1"
-> name = "llama2-7b-hf"
+> name = "Llama-2-7b-hf"
 > ```
 
 Currently, these models are provided as part of the core library:

website/docs/api/legacy.mdx

@@ -162,7 +162,10 @@ network has an internal CNN Tok2Vec layer and uses attention.
 
 Since `spacy.TextCatCNN.v2`, this architecture has become resizable, which means
 that you can add labels to a previously trained textcat. `TextCatCNN` v1 did not
-yet support that.
+yet support that. `TextCatCNN` has been replaced by the more general
+[`TextCatReduce`](/api/architectures#TextCatReduce) layer. `TextCatCNN` is
+identical to `TextCatReduce` with `use_reduce_mean=true`,
+`use_reduce_first=false`, `reduce_last=false` and `use_reduce_max=false`.
 
 > #### Example Config
 >
@@ -194,11 +197,58 @@ architecture is usually less accurate than the ensemble, but runs faster.
 | `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
 | **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
 
+### spacy.TextCatCNN.v2 {id="TextCatCNN_v2"}
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.TextCatCNN.v2"
+> exclusive_classes = false
+> nO = null
+>
+> [model.tok2vec]
+> @architectures = "spacy.HashEmbedCNN.v2"
+> pretrained_vectors = null
+> width = 96
+> depth = 4
+> embed_size = 2000
+> window_size = 1
+> maxout_pieces = 3
+> subword_features = true
+> ```
+
+A neural network model where token vectors are calculated using a CNN. The
+vectors are mean pooled and used as features in a feed-forward network. This
+architecture is usually less accurate than the ensemble, but runs faster.
+
+`TextCatCNN` has been replaced by the more general
+[`TextCatReduce`](/api/architectures#TextCatReduce) layer. `TextCatCNN` is
+identical to `TextCatReduce` with `use_reduce_mean=true`,
+`use_reduce_first=false`, `reduce_last=false` and `use_reduce_max=false`.
+
+| Name                | Description                                                                                                                                                                                    |
+| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~                                                                                                                                     |
+| `tok2vec`           | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~                                                                                                                                        |
+| `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
+| **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
+
+<Accordion title="spacy.TextCatCNN.v1 definition" spaced>
+
+[TextCatCNN.v1](/api/legacy#TextCatCNN_v1) had the exact same signature, but was
+not yet resizable. Since v2, new labels can be added to this component, even
+after training.
+
+</Accordion>
+
 ### spacy.TextCatBOW.v1 {id="TextCatBOW_v1"}
 
 Since `spacy.TextCatBOW.v2`, this architecture has become resizable, which means
 that you can add labels to a previously trained textcat. `TextCatBOW` v1 did not
-yet support that.
+yet support that. Versions of this model before `spacy.TextCatBOW.v3` used an
+erroneous sparse linear layer that only used a small number of the allocated
+parameters.
 
 > #### Example Config
 >
@@ -222,6 +272,33 @@ the others, but may not be as accurate, especially if texts are short.
 | `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
 | **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
 
+### spacy.TextCatBOW.v2 {id="TextCatBOW"}
+
+Versions of this model before `spacy.TextCatBOW.v3` used an erroneous sparse
+linear layer that only used a small number of the allocated parameters.
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.TextCatBOW.v2"
+> exclusive_classes = false
+> ngram_size = 1
+> no_output_layer = false
+> nO = null
+> ```
+
+An n-gram "bag-of-words" model. This architecture should run much faster than
+the others, but may not be as accurate, especially if texts are short.
+
+| Name                | Description                                                                                                                                                                                    |
+| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~                                                                                                                                     |
+| `ngram_size`        | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3` would give unigram, trigram and bigram features. ~~int~~                                           |
+| `no_output_layer`   | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`). ~~bool~~                                                          |
+| `nO`                | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
+| **CREATES**         | The model using the architecture. ~~Model[List[Doc], Floats2d]~~                                                                                                                               |
+
 ### spacy.TransitionBasedParser.v1 {id="TransitionBasedParser_v1"}
 
 Identical to

website/docs/api/transformer.mdx

@@ -397,6 +397,17 @@ are wrapped into the
 by this class. Instances of this class are typically assigned to the
 [`Doc._.trf_data`](/api/transformer#assigned-attributes) extension attribute.
 
+> #### Example
+>
+> ```python
+> # Get the last hidden layer output for "is" (token index 1)
+> doc = nlp("This is a text.")
+> indices = doc._.trf_data.align[1].data.flatten()
+> last_hidden_state = doc._.trf_data.model_output.last_hidden_state
+> dim = last_hidden_state.shape[-1]
+> tensors = last_hidden_state.reshape(-1, dim)[indices]
+> ```
+
 | Name           | Description                                                                                                                                                                                                                                                                                                                          |
 | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `tokens`       | A slice of the tokens data produced by the tokenizer. This may have several fields, including the token IDs, the texts and the attention mask. See the [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) object for details. ~~dict~~                       |

website/docs/models/index.mdx

@@ -108,12 +108,12 @@ In the `sm`/`md`/`lg` models:
 
 #### CNN/CPU pipelines with floret vectors
 
-The Finnish, Korean and Swedish `md` and `lg` pipelines use
-[floret vectors](/usage/v3-2#vectors) instead of default vectors. If you're
-running a trained pipeline on texts and working with [`Doc`](/api/doc) objects,
-you shouldn't notice any difference with floret vectors. With floret vectors no
-tokens are out-of-vocabulary, so [`Token.is_oov`](/api/token#attributes) will
-return `False` for all tokens.
+The Croatian, Finnish, Korean, Slovenian, Swedish and Ukrainian `md` and `lg`
+pipelines use [floret vectors](/usage/v3-2#vectors) instead of default vectors.
+If you're running a trained pipeline on texts and working with [`Doc`](/api/doc)
+objects, you shouldn't notice any difference with floret vectors. With floret
+vectors no tokens are out-of-vocabulary, so
+[`Token.is_oov`](/api/token#attributes) will return `False` for all tokens.
 
 If you access vectors directly for similarity comparisons, there are a few
 differences because floret vectors don't include a fixed word list like the
@@ -132,10 +132,20 @@ vector keys for default vectors.
 
 ### Transformer pipeline design {id="design-trf"}
 
-In the transformer (`trf`) models, the `tagger`, `parser` and `ner` (if present)
-all listen to the `transformer` component. The `attribute_ruler` and
+In the transformer (`trf`) pipelines, the `tagger`, `parser` and `ner` (if
+present) all listen to the `transformer` component. The `attribute_ruler` and
 `lemmatizer` have the same configuration as in the CNN models.
 
+For spaCy v3.0-v3.6, `trf` pipelines use
+[`spacy-transformers`](https://github.com/explosion/spacy-transformers) and the
+transformer output in `doc._.trf_data` is a
+[`TransformerData`](/api/transformer#transformerdata) object.
+
+For spaCy v3.7+, `trf` pipelines use
+[`spacy-curated-transformers`](https://github.com/explosion/spacy-curated-transformers)
+and `doc._.trf_data` is a
+[`DocTransformerOutput`](/api/curatedtransformer#doctransformeroutput) object.
+
 ### Modifying the default pipeline {id="design-modify"}
 
 For faster processing, you may only want to run a subset of the components in a

website/docs/usage/large-language-models.mdx

@@ -340,15 +340,30 @@ A _task_ defines an NLP problem or question, that will be sent to the LLM via a
 prompt. Further, the task defines how to parse the LLM's responses back into
 structured information. All tasks are registered in the `llm_tasks` registry.
 
-Practically speaking, a task should adhere to the `Protocol` `LLMTask` defined
-in [`ty.py`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/ty.py).
-It needs to define a `generate_prompts` function and a `parse_responses`
-function.
+Practically speaking, a task should adhere to the `Protocol` named `LLMTask`
+defined in
+[`ty.py`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/ty.py). It
+needs to define a `generate_prompts` function and a `parse_responses` function.
 
-| Task                                                                        | Description                                                                                                                                                  |
-| --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| [`task.generate_prompts`](/api/large-language-models#task-generate-prompts) | Takes a collection of documents, and returns a collection of "prompts", which can be of type `Any`.                                                          |
-| [`task.parse_responses`](/api/large-language-models#task-parse-responses)   | Takes a collection of LLM responses and the original documents, parses the responses into structured information, and sets the annotations on the documents. |
+Tasks may support prompt sharding (for more info see the API docs on
+[sharding](/api/large-language-models#task-sharding) and
+[non-sharding](/api/large-language-models#task-nonsharding) tasks). The function
+signatures for `generate_prompts` and `parse_responses` depend on whether they
+do.
+
+For tasks **not supporting** sharding:
+
+| Task                                                                                    | Description                                                                                                                                                  |     |
+| --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | --- |
+| [`task.generate_prompts`](/api/large-language-models#task-nonsharding-generate-prompts) | Takes a collection of documents, and returns a collection of prompts, which can be of type `Any`.                                                            |
+| [`task.parse_responses`](/api/large-language-models#task-nonsharding-parse-responses)   | Takes a collection of LLM responses and the original documents, parses the responses into structured information, and sets the annotations on the documents. |
+
+For tasks **supporting** sharding:
+
+| Task                                                                                 | Description                                                                                                                                                                                                                                                  |     |
+| ------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --- |
+| [`task.generate_prompts`](/api/large-language-models#task-sharding-generate-prompts) | Takes a collection of documents, and returns a collection of collections of prompt shards, which can be of type `Any`.                                                                                                                                       |
+| [`task.parse_responses`](/api/large-language-models#task-sharding-parse-responses)   | Takes a collection of collections of LLM responses (one per prompt shard) and the original documents, parses the responses into structured information, sets the annotations on the doc shards, and merges those doc shards back into a single doc instance. |
 
 Moreover, the task may define an optional [`scorer` method](/api/scorer#score).
 It should accept an iterable of `Example` objects as input and return a score
@@ -357,6 +372,7 @@ evaluate the component.
 
 | Component                                                               | Description                                                                                                       |
 | ----------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
+| [`spacy.EntityLinker.v1`](/api/large-language-models#el-v1)             | The entity linking task prompts the model to link all entities in a given text to entries in a knowledge base.    |
 | [`spacy.Summarization.v1`](/api/large-language-models#summarization-v1) | The summarization task prompts the model for a concise summary of the provided text.                              |
 | [`spacy.NER.v3`](/api/large-language-models#ner-v3)                     | Implements Chain-of-Thought reasoning for NER extraction - obtains higher accuracy than v1 or v2.                 |
 | [`spacy.NER.v2`](/api/large-language-models#ner-v2)                     | Builds on v1 and additionally supports defining the provided labels with explicit descriptions.                   |
@@ -369,7 +385,9 @@ evaluate the component.
 | [`spacy.TextCat.v2`](/api/large-language-models#textcat-v2)             | Version 2 builds on v1 and includes an improved prompt template.                                                  |
 | [`spacy.TextCat.v1`](/api/large-language-models#textcat-v1)             | Version 1 of the built-in TextCat task supports both zero-shot and few-shot prompting.                            |
 | [`spacy.Lemma.v1`](/api/large-language-models#lemma-v1)                 | Lemmatizes the provided text and updates the `lemma_` attribute of the tokens accordingly.                        |
+| [`spacy.Raw.v1`](/api/large-language-models#raw-v1)                     | Executes raw doc content as prompt to LLM.                                                                        |
 | [`spacy.Sentiment.v1`](/api/large-language-models#sentiment-v1)         | Performs sentiment analysis on provided texts.                                                                    |
+| [`spacy.Translation.v1`](/api/large-language-models#translation-v1)     | Translates doc content into the specified target language.                                                        |
 | [`spacy.NoOp.v1`](/api/large-language-models#noop-v1)                   | This task is only useful for testing - it tells the LLM to do nothing, and does not set any fields on the `docs`. |
 
 #### Providing examples for few-shot prompts {id="few-shot-prompts"}

website/docs/usage/layers-architectures.mdx

@@ -153,8 +153,9 @@ maxout_pieces = 3
 depth = 2
 
 [components.textcat.model.linear_model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 ```
@@ -170,8 +171,9 @@ factory = "textcat"
 labels = []
 
 [components.textcat.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 nO = null

website/docs/usage/processing-pipelines.mdx

@@ -1328,8 +1328,9 @@ labels = []
 # This function is created and then passed to the "textcat" component as
 # the argument "model"
 [components.textcat.model]
-@architectures = "spacy.TextCatBOW.v2"
+@architectures = "spacy.TextCatBOW.v3"
 exclusive_classes = true
+length = 262144
 ngram_size = 1
 no_output_layer = false
 

website/docs/usage/saving-loading.mdx

@@ -405,7 +405,7 @@ available to spaCy, all you need to do is install the package in your
 environment:
 
 ```bash
-$ python setup.py develop
+$ python -m pip install .
 ```
 
 spaCy is now able to create the pipeline component `"snek"` – even though you
@@ -673,7 +673,7 @@ $ python -m spacy package ./en_example_pipeline ./packages
 ```
 
 This command will create a pipeline package directory and will run
-`python setup.py sdist` in that directory to create a binary `.whl` file or
+`python -m build` in that directory to create a binary `.whl` file or
 `.tar.gz` archive of your package that can be installed using `pip install`.
 Installing the binary wheel is usually more efficient.
 

website/meta/languages.json

@@ -103,6 +103,10 @@
             "has_examples": true,
             "models": ["fi_core_news_sm", "fi_core_news_md", "fi_core_news_lg"]
         },
+        {
+            "code": "fo",
+            "name": "Faroese"
+        },
         {
             "code": "fr",
             "name": "French",
@@ -290,6 +294,12 @@
             "example": "Dit is een zin.",
             "has_examples": true
         },
+        {
+            "code": "nn",
+            "name": "Norwegian Nynorsk",
+            "example": "Det er ein meir enn i same periode i fjor.",
+            "has_examples": true
+        },
         {
             "code": "pl",
             "name": "Polish",

website/meta/site.json

@@ -66,6 +66,10 @@
                 {
                     "text": "Stack Overflow",
                     "url": "http://stackoverflow.com/questions/tagged/spacy"
+                },
+                {
+                    "text": "Merchandise",
+                    "url": "https://explosion.ai/merch"
                 }
             ]
         },

website/meta/universe.json

@@ -4500,6 +4500,23 @@
                 "website": "https://nlp.unibuc.ro/people/snisioi.html"
             },
             "category": ["pipeline", "training", "models"]
+        },
+        {
+            "id": "redfield-spacy-nodes",
+            "title": "Redfield NLP Nodes for KNIME",
+            "slogan": "Makes the functionality of the spaCy library available in KNIME Analytics Platform.",
+            "description": "This extension provides nodes that make the functionality of the spaCy library available in the [KNIME Analytics Platform](https://www.knime.com/).",
+            "github": "Redfield-AB/Spacy-Nodes",
+            "url": "https://redfield.ai/spacy-redfield/",
+            "thumb": "https://raw.githubusercontent.com/Redfield-AB/Spacy-Nodes/master/resource/redfield_logo_100x100.png",
+            "image": "https://raw.githubusercontent.com/Redfield-AB/Spacy-Nodes/master/resource/screen1.png",
+            "author": "Redfield AB",
+            "author_links": {
+                "twitter": "Redfield_AB",
+                "github": "Redfield-AB",
+                "website": "https://redfield.ai"
+            },
+            "category": ["standalone"]
         }
     ],