Bump spacy from 2.2.4 to 3.8.2

[dependabot]

Bumps spacy from 2.2.4 to 3.8.2.

Release notes

Sourced from spacy's releases.

v3.8: Memory management for persistent services, numpy 2.0 support

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.

... (truncated)

Commits

• 63f1b53 Check test failure
• 0cdcfe5 Set version to v3.8.2
• 924cbc9 Fix environment variable for test
• e1d0505 Fix requirements.txt
• 6c038aa Don't disable tests on workflow changes
• f0084b9 Fix matrix in tests
• ff81bfb Update tests
• 9c5b61b isort
• 725ccba Format
• a8837be Set version to v3.8.1
• Additional commits viewable in compare view

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