Bump chardet from 5.2.0 to 7.4.0.post2

[dependabot]

Bumps chardet from 5.2.0 to 7.4.0.post2.

Release notes

Sourced from chardet's releases.

chardet 7.4.0 brings accuracy up to 99.3% (from 98.6% in 7.3.0) and significantly faster cold start thanks to a new dense model format.

What's New

Performance:

• New dense zlib-compressed model format (v2) drops cold start (import + first detect) from ~75ms to ~13ms with mypyc

Accuracy (98.6% → 99.3%):

• Eliminated train/test data overlap via content fingerprinting
• Added MADLAD-400 and Wikipedia as supplemental training sources
• Improved non-ASCII bigram scoring: high-byte bigrams are now preserved during training and weighted by per-bigram IDF
• Encoding-aware substitution filtering (substitutions only apply for characters the target encoding can't represent)
• Increased training samples from 15K to 25K per language/encoding pair

Bug fixes:

• Added dedicated structural analyzers for CP932, CP949, and Big5-HKSCS (these were previously sharing their base encoding's byte-range analyzer, missing extended ranges)

Metrics

	chardet 7.4.0 (mypyc)	chardet 6.0.0	charset-normalizer 3.4.6
Accuracy (2,517 files)	99.3%	88.2%	85.4%
Speed	551 files/s	12 files/s	376 files/s
Language detection	95.7%	40.0%	59.2%

Full changelog: https://chardet.readthedocs.io/en/latest/changelog.html

7.3.0

License

• 0BSD license — the project license has been changed from MIT to 0BSD, a maximally permissive license with no attribution requirement. All prior 7.x releases should also be considered 0BSD licensed as of this release.

Features

• Added mime_type field to detection results — identifies file types for both binary (via magic number matching) and text content. Returned in all detect(), detect_all(), and UniversalDetector results. (#350)
• New pipeline/magic.py module detects 40+ binary file formats including images, audio/video, archives, documents, executables, and fonts. ZIP-based formats (XLSX, DOCX, JAR, APK, EPUB, wheel, OpenDocument) are distinguished by entry filenames. (#350)

Bug Fixes

• Fixed incorrect equivalence between UTF-16-LE and UTF-16-BE in accuracy testing — these are distinct encodings with different byte order, not interchangeable

Performance

• Added 4 new modules to mypyc compilation (orchestrator, confusion, magic, ascii), bringing the total to 11 compiled modules
• Capped statistical scoring at 16 KB — bigram models converge quickly, so large files no longer score the full 200 KB. Worst-case detection time dropped from 62ms to 26ms with no accuracy loss.
• Replaced dataclasses.replace() with direct DetectionResult construction on hot paths, eliminating ~354k function calls per full test suite run

Build

... (truncated)

Changelog

Sourced from chardet's changelog.

Changelog

.. note::

Entries marked "via Claude" were developed with Claude Code <https://claude.ai/code>_. Dan directed the design, reviewed all output, and takes responsibility for the result. Unmarked entries by Dan were written without AI assistance.

7.4.0 (2026-03-26)

Performance:

• Switched to dense zlib-compressed model format (v2): models are now stored as contiguous memoryview slices of a single decompressed blob, eliminating per-model struct.unpack overhead. Cold start (import + first detect) dropped from ~75ms to ~13ms with mypyc. (Dan Blanchard <https://github.com/dan-blanchard>_ via Claude, [#354](https://github.com/chardet/chardet/issues/354) <https://github.com/chardet/chardet/pull/354>_)

Accuracy:

• Accuracy improved from 98.6% to 99.3% (2499/2517 files) through a combination of training and scoring improvements:

  • Eliminated train/test data overlap by content-fingerprinting test suite articles and excluding them from training data ([#351](https://github.com/chardet/chardet/issues/351) <https://github.com/chardet/chardet/pull/351>_)
  • Added MADLAD-400 and Wikipedia as supplemental training sources to fill gaps left by exclusion filtering ([#351](https://github.com/chardet/chardet/issues/351) <https://github.com/chardet/chardet/pull/351>_)
  • Improved non-ASCII bigram scoring: high-byte bigrams are now preserved during training (instead of being crushed by global normalization), and weighted by per-bigram IDF so encoding-specific byte patterns contribute proportionally to how discriminative they are ([#352](https://github.com/chardet/chardet/issues/352) <https://github.com/chardet/chardet/pull/352>_)
  • Added encoding-aware substitution filtering: character substitutions during training now only apply for characters the target encoding cannot represent
  • Increased training samples from 15K to 25K per language/encoding pair (Dan Blanchard <https://github.com/dan-blanchard>_ via Claude)

Bug Fixes:

• Added dedicated structural analyzers for CP932, CP949, and Big5-HKSCS: these superset encodings previously shared their base encoding's byte-range analyzer, missing extended ranges unique to each superset

... (truncated)

Commits

• e37cf3c fix: prevent dirty-tree version in Windows mypyc wheel builds
• f9f5af2 Fix a couple errors in the changelog
• 53755de chore: add .superpowers/ to .gitignore
• 3a20df6 docs: update README examples with correct outputs
• 17ec933 docs: note train/test separation in performance.rst
• 7296d93 docs: add footnote explaining 0 B import memory (lazy loading)
• 4221536 docs: add chardet 7.0.1-7.3.0 to historical performance table
• 9dfb65b docs: remove historical performance table spec/plan (preserved in git history)
• 72413b8 docs: add historical performance table to performance.rst
• cbcb80d fix: handle None detect() results and missing venvs in benchmarks
• Additional commits viewable in compare view

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 54.73%. Comparing base (520ab66) to head (35fce62).
✅ All tests successful. No failed tests found.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1662   +/-   ##
=======================================
  Coverage   54.73%   54.73%           
=======================================
  Files         335      335           
  Lines       27401    27401           
=======================================
+ Hits        14997    14998    +1     
+ Misses      12404    12403    -1     

Flag	Coverage Δ
functionaltests	0.00% <ø> (ø)
unittests	54.73% <ø> (+<0.01%)	⬆️

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