In order to support Python 3.13, spaCy is now compiled with Cython 3. This brings a change to the way types are handled at runtime (Cython 3 uses the from __future__ import annotations semantics, which stores types as strings at runtime. This difference caused problems for components registered within Cython files, as we rely on building Pydantic models from factory function signatures to do validation.

To support Python 3.13 we therefore create a new module, spacy.pipeline.factories, which contains the factory function implementations. __getattr__ import shims have been added to the previous locations of these functions to prevent backwards incompatibilities.

As well as moving the factories, the new implementation avoids import-time side-effects, by moving the actual calls to the decorator inside a function, which is executed once when the Language class is initialised.

A matching change has been made to the catalogue registry decorators. A new module spacy.registrations has been created that performs all the catalogue registrations. Moving these registrations away from the functions prevents these decorators from running at import time. This change was not necessary for the Python 3.13 support, but it means we no longer rely on any import-time side-effects, which will allow us to improve spaCy's import time and therefore CLI execution time. The change also makes maintenance easier as it's easier to find the implementations of different registry functions (this may help library users as well).

Restores support for wheels for ARM platforms, while correctly noting compatibility range.

Fix bug in memory zones when non-transient strings were added to the StringStore inside a memory zone. This caused a bug in the morphological analyser that caused string not found errors when applied during a memory zone.

Optional memory management for persistent services

Support a new context manager method Language.memory_zone(), to allow long-running services to avoid growing memory usage from cached entries in the Vocab or StringStore. Once the memory zone block ends, spaCy will evict Vocab and StringStore entries that were added during the block, freeing up memory. Doc objects created inside a memory zone block should not be accessed outside the block.

The current implementation disables population of the tokenizer cache inside the memory zone, resulting in some performance impact. The performance difference will likely be negligible if you're running a full pipeline, but if you're only running the tokenizer, it'll be much slower. If this is a problem, you can mitigate it by warming the cache first, by processing the first few batches of text without creating a memory zone. Support for memory zones in the tokenizer will be added in a future update.

The Language.memory_zone() context manager also checks for a memory_zone() method on pipeline components, so that components can perform similar memory management if necessary. None of the built-in components currently require this.

If you component needs to add non-transient entries to the StringStore or Vocab, you can pass the allow_transient=False flag to the Vocab.add() or StringStore.add() components.

Example usage:

import spacy
import json
from pathlib import Path
from typing import Iterator
from collections import Counter
import typer
from spacy.util import minibatch


def texts(path: Path) -> Iterator[str]:
    with path.open("r", encoding="utf8") as file_:
        for line in file_:
            yield json.loads(line)["text"]

def main(jsonl_path: Path) -> None:
    nlp = spacy.load("en_core_web_sm")
    counts = Counter()
    batches = minibatch(texts(jsonl_path), 1000)
    for i, batch in enumerate(batches):
        print("Batch", i)
        with nlp.memory_zone():
            for doc in nlp.pipe(batch):
                for token in doc:
                    counts[token.text] += 1
    for word, count in counts.most_common(100):
        print(count, word)

if __name__ == "__main__":
    typer.run(main)

Numpy v2 compatibility

Numpy 2.0 isn't binary-compatible with numpy v1, so we need to build against one or the other. This release isolates the dependency change and has no other changes, to make things easier if the dependency change causes problems.

This dependency change was previously attempted in version 3.7.6, but dependencies within the v3.7 family of models resulted in some conflicts, and some packages depending on numpy v1 were incompatible with v3.7.6. I've therefore removed the 3.7.6 release and replaced it with this one, which increments the minor version.

Model packages no longer list spacy as a requirement

I've also made a change to the way models are packaged to make it easier to release more quickly. Previously spaCy models specified a versioned requirement on spacy itself. This meant that there was no way to increment the spaCy version and have it work with the existing models, because the models would specify they were only compatible with spacy>=3.7.0,<3.8.0. We have a compatibility table that allows spacy to see which models are compatible, but the models themselves can't know which future versions of spaCy they work with.

I've therefore added a flag --require-parent/--no-require-parent to the spacy package CLI, which controls where the parent package (e.g. spaCy) should be listed as a requirement of the model. --require-parent is the default for v3.8, but this will change to --no-require-parent by default in v4. I've set --no-require-parent for the v3.8 models, so that further changes can be published that don't impact the models, without retraining the models or forcing users to redownload them.

Support a new context manager method Language.memory_zone(), to allow long-running services to avoid growing memory usage from cached entries in the Vocab or StringStore. Once the memory zone block ends, spaCy will evict Vocab and StringStore entries that were added during the block, freeing up memory. Doc objects created inside a memory zone block should not be accessed outside the block.

The current implementation disables population of the tokenizer cache inside the memory zone, resulting in some performance impact. The performance difference will likely be negligible if you're running a full pipeline, but if you're only running the tokenizer, it'll be much slower. If this is a problem, you can mitigate it by warming the cache first, by processing the first few batches of text without creating a memory zone. Support for memory zones in the tokenizer will be added in a future update.

The Language.memory_zone() context manager also checks for a memory_zone() method on pipeline components, so that components can perform similar memory management if necessary. None of the built-in components currently require this.

If you component needs to add non-transient entries to the StringStore or Vocab, you can pass the allow_transient=False flag to the Vocab.add() or StringStore.add() components.

Example usage:

import spacy
import json
from pathlib import Path
from typing import Iterator
from collections import Counter
import typer
from spacy.util import minibatch


def texts(path: Path) -> Iterator[str]:
    with path.open("r", encoding="utf8") as file_:
        for line in file_:
            yield json.loads(line)["text"]

def main(jsonl_path: Path) -> None:
    nlp = spacy.load("en_core_web_sm")
    counts = Counter()
    batches = minibatch(texts(jsonl_path), 1000)
    for i, batch in enumerate(batches):
        print("Batch", i)
        with nlp.vocab.memory_zone():
            for doc in nlp.pipe(batch):
                for token in doc:
                    counts[token.text] += 1
    for word, count in counts.most_common(100):
        print(count, word)

if __name__ == "__main__":
    typer.run(main)```

✨ New features and improvements

• Sanitize direct download for spacy download (#13313).
• Convert Cython properties to decorator syntax (#13390).
• Bump Weasel pin to allow v0.4.x (#13409).
• Improvements to the test suite (#13469, #13470).
• Bump Typer pin to allow v0.10.0 and above (#13471).
• Allow typing-extensions<5.0.0 for Python < 3.8 (#13516).

🔴 Bug fixes

• #13400: Fix use_gold_ents behaviour for EntityLinker.

📖 Documentation and examples

• Make the file name for code listings stick to the top (#13379).
• Update the documentation of MorphAnalysis (#13433).
• Typo fixes in the documentation (#13466).

👥 Contributors

@danieldk, @honnibal, @ines, @JoeSchiff, @nokados, @Paillat-dev, @rmitsch, @schorfma, @strickvl, @svlandeg, @ynx0

✨ New features and improvements

• Improve NumPy 2.0 compatibility (#13103).
• Added language extensions for Faroese and Norwegian Nynorsk (#13116).
• Add new TextCatReduce.v1 layer for text classification (#13181).
• Add new TextCatParametricAttention.v1 layer for text classification (#13201).
• Use build module for creating model packages by default (#13109).
• Add support for code loading to the benchmark speed command (#13247).
• Extend lexical attributes for English with more numericals (#13106).
• Warn about reloading dependencies after downloading models (#13081).

🔴 Bug fixes

• #13259, #13304, #13321: Correctness fixes for multiprocessing support in Language.pipe.
• #13187: Typing and documentation fixes for Doc.
• #13086: Update Tokenizer.explain for special cases with whitespace.
• #13068: Fix displaCy span stacking.
• #13149: Add spacy.TextCatBOW.v3 to use the fixed SparseLinear layer.

📖 Documentation and examples

• Many improvements and updates to the LLM documentation.
• Update trf_data examples and the transformer pipeline design section.

👥 Contributors

@adrianeboyd, @danieldk, @evornov, @honnibal, @ines, @lise-brinck, @ridge-kimani, @rmitsch, @shadeMe, @svlandeg

✨ New features and improvements

• Update __all__ fields (#13063).

🔴 Bug fixes

• #13035: Remove Pathy requirement.
• #13053: Restore spacy.cli.project API.
• #13057: Support Any comparisons for Token and Span.

📖 Documentation and examples

• Many updates for spacy-llm including Azure OpenAI, PaLM, and Mistral support.
• Various documentation corrections.

👥 Contributors

@adrianeboyd, @honnibal, @ines, @rmitsch, @svlandeg

🔴 Bug fixes

• Revert lazy loading of CLI module for spacy.info to fix availability of spacy.cli following import spacy (#13040).

👥 Contributors

@adrianeboyd, @honnibal, @ines, @svlandeg