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Bump the pip group across 3 directories with 5 updates#4324

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Bump the pip group across 3 directories with 5 updates#4324
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Bumps the pip group with 2 updates in the /src/dependencies/requirements/base_requirements directory: setuptools and torch.
Bumps the pip group with 1 update in the /src/dependencies/requirements/generated_requirements directory: torch.
Bumps the pip group with 3 updates in the /src/maxtext/inference/mlperf directory: sentencepiece, transformers and nltk.

Updates setuptools from 78.1.0 to 78.1.1

Changelog

Sourced from setuptools's changelog.

v78.1.1

Bugfixes

  • More fully sanitized the filename in PackageIndex._download. (#4946)
Commits

Updates torch from 2.11.0 to 2.12.1

Release notes

Sourced from torch's releases.

PyTorch 2.12.1 Release, bug fix release

This release is meant to fix the following regressions and silent correctness issues:

Regression fixes

  • Fix nondeterministic outputs in test_batch_invariance with FLASH_ATTN on NVIDIA B200 GPUs (#181248), fixed by updating Triton to 3.7.1 (#186814)
  • Fix illegal memory access in the Triton convolution2d_bwd_weight kernel on B100/B200 (sm100) GPUs (#187081), fixed by updating Triton to 3.7.1 (#186814)
  • Fix fill_ on byte-dtype views with misaligned storage offset (#186821)

Releng / Build

  • Drop CPython 3.13t from the binary build matrix (#182951)

PyTorch 2.12.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

Build Frontend

  • Strengthened SVE compile checks in FindARM.cmake, which may reject previously accepted but incorrect SVE configurations (#176646)

    Source builds that enable SVE now validate the compiler configuration more strictly. If a build previously passed with an incomplete or mismatched SVE setup, it may now fail during CMake configuration instead of later in compilation. Update the compiler/toolchain flags so they accurately describe the target SVE support, or disable SVE for that build.

  • Updated the minimum CUDA version required to build PyTorch from source to CUDA 12.6 (#178925)

    Building PyTorch from source with CUDA versions older than 12.6 is no longer supported. Users building custom binaries should install CUDA 12.6 or newer and make sure CUDA_HOME points to that installation.

    Version 2.11:

    CUDA_HOME=/usr/local/cuda-12.4 python setup.py develop

... (truncated)

Commits

Updates setuptools from 78.1.0 to 78.1.1

Changelog

Sourced from setuptools's changelog.

v78.1.1

Bugfixes

  • More fully sanitized the filename in PackageIndex._download. (#4946)
Commits

Updates torch from 2.11.0 to 2.12.1

Release notes

Sourced from torch's releases.

PyTorch 2.12.1 Release, bug fix release

This release is meant to fix the following regressions and silent correctness issues:

Regression fixes

  • Fix nondeterministic outputs in test_batch_invariance with FLASH_ATTN on NVIDIA B200 GPUs (#181248), fixed by updating Triton to 3.7.1 (#186814)
  • Fix illegal memory access in the Triton convolution2d_bwd_weight kernel on B100/B200 (sm100) GPUs (#187081), fixed by updating Triton to 3.7.1 (#186814)
  • Fix fill_ on byte-dtype views with misaligned storage offset (#186821)

Releng / Build

  • Drop CPython 3.13t from the binary build matrix (#182951)

PyTorch 2.12.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

Build Frontend

  • Strengthened SVE compile checks in FindARM.cmake, which may reject previously accepted but incorrect SVE configurations (#176646)

    Source builds that enable SVE now validate the compiler configuration more strictly. If a build previously passed with an incomplete or mismatched SVE setup, it may now fail during CMake configuration instead of later in compilation. Update the compiler/toolchain flags so they accurately describe the target SVE support, or disable SVE for that build.

  • Updated the minimum CUDA version required to build PyTorch from source to CUDA 12.6 (#178925)

    Building PyTorch from source with CUDA versions older than 12.6 is no longer supported. Users building custom binaries should install CUDA 12.6 or newer and make sure CUDA_HOME points to that installation.

    Version 2.11:

    CUDA_HOME=/usr/local/cuda-12.4 python setup.py develop

... (truncated)

Commits

Updates torch from 2.11.0+cpu to 2.12.1

Release notes

Sourced from torch's releases.

PyTorch 2.12.1 Release, bug fix release

This release is meant to fix the following regressions and silent correctness issues:

Regression fixes

  • Fix nondeterministic outputs in test_batch_invariance with FLASH_ATTN on NVIDIA B200 GPUs (#181248), fixed by updating Triton to 3.7.1 (#186814)
  • Fix illegal memory access in the Triton convolution2d_bwd_weight kernel on B100/B200 (sm100) GPUs (#187081), fixed by updating Triton to 3.7.1 (#186814)
  • Fix fill_ on byte-dtype views with misaligned storage offset (#186821)

Releng / Build

  • Drop CPython 3.13t from the binary build matrix (#182951)

PyTorch 2.12.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

Build Frontend

  • Strengthened SVE compile checks in FindARM.cmake, which may reject previously accepted but incorrect SVE configurations (#176646)

    Source builds that enable SVE now validate the compiler configuration more strictly. If a build previously passed with an incomplete or mismatched SVE setup, it may now fail during CMake configuration instead of later in compilation. Update the compiler/toolchain flags so they accurately describe the target SVE support, or disable SVE for that build.

  • Updated the minimum CUDA version required to build PyTorch from source to CUDA 12.6 (#178925)

    Building PyTorch from source with CUDA versions older than 12.6 is no longer supported. Users building custom binaries should install CUDA 12.6 or newer and make sure CUDA_HOME points to that installation.

    Version 2.11:

    CUDA_HOME=/usr/local/cuda-12.4 python setup.py develop

... (truncated)

Commits

Updates torch from 2.11.0+cpu to 2.12.1

Release notes

Sourced from torch's releases.

PyTorch 2.12.1 Release, bug fix release

This release is meant to fix the following regressions and silent correctness issues:

Regression fixes

  • Fix nondeterministic outputs in test_batch_invariance with FLASH_ATTN on NVIDIA B200 GPUs (#181248), fixed by updating Triton to 3.7.1 (#186814)
  • Fix illegal memory access in the Triton convolution2d_bwd_weight kernel on B100/B200 (sm100) GPUs (#187081), fixed by updating Triton to 3.7.1 (#186814)
  • Fix fill_ on byte-dtype views with misaligned storage offset (#186821)

Releng / Build

  • Drop CPython 3.13t from the binary build matrix (#182951)

PyTorch 2.12.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

Build Frontend

  • Strengthened SVE compile checks in FindARM.cmake, which may reject previously accepted but incorrect SVE configurations (#176646)

    Source builds that enable SVE now validate the compiler configuration more strictly. If a build previously passed with an incomplete or mismatched SVE setup, it may now fail during CMake configuration instead of later in compilation. Update the compiler/toolchain flags so they accurately describe the target SVE support, or disable SVE for that build.

  • Updated the minimum CUDA version required to build PyTorch from source to CUDA 12.6 (#178925)

    Building PyTorch from source with CUDA versions older than 12.6 is no longer supported. Users building custom binaries should install CUDA 12.6 or newer and make sure CUDA_HOME points to that installation.

    Version 2.11:

    CUDA_HOME=/usr/local/cuda-12.4 python setup.py develop

... (truncated)

Commits

Updates sentencepiece from 0.1.99 to 0.2.1

Release notes

Sourced from sentencepiece's releases.

v0.2.1

Major changes

New features

  • [ALL]: Added new build mode to prevent the precompiled normalization rules being embedded in *.so and *.a. (-DSPM_DISABLE_EMBEDDED_DATA=ON). This reduces the runtime size by approximately 1-2 MB. This mode is enabled to build python wheels. The rules are loaded as the data package.

Bug fixes & minor changes

  • [ALL]: Security fix to address a heap overflow issue that could occur when using a model containing an invalid precompiled normalization model.
  • [Python]: Deprecates the wheel package for Linux i686.
  • [Python]: Supported wheel for Windows Arm64. #1114
  • [Python]: Fixed the crash issue on batch decoding #1051
  • [ALL]: Updated the Unicode normalization rule with the latest ICU/Unicode rules.
  • [ALL]: Unused code and build mode cleanup.

v0.2.1pre2

Major changes

New features

  • [ALL]: Added new build mode to prevent the precompiled normalization rules being embedded in *.so and *.a. (-DSPM_DISABLE_EMBEDDED_DATA=ON). This reduces the runtime size by approximately 1-2 MB. This mode is enabled to build python wheels. The rules are loaded as the data package.

Bug fixes & minor changes

  • [ALL]: Security fix to address a heap overflow issue that could occur when using a model containing an invalid precompiled normalization model.
  • [Python]: Deprecates the wheel package for Linux i686.
  • [Python]: Supported wheel for Windows Arm64.
  • [Python]: Fixed the crash issue on batch decoding #1051
  • [ALL]: Updated the Unicode normalization rule with the latest ICU/Unicode rules.
  • [ALL]: Unused code and build mode cleanup.

v0.2.0

Major changes

N/A

New features

  • [ALL] Added SentencePieceNormalizer class in C++/Python. It supports almost the equivalent feature of spm_normalize. Python Sample C++ Sample
  • [ALL] Added SentencePieceProcessor::Normalize method in C++/Python Python Sample C++ Sample
  • [ALL] Added functionality to override the normalization spec before the processing. Python Sample

Bug fixes & minor changes

... (truncated)

Commits
  • 31646a4 Merge pull request #1136 from crusaderky/pytest-run-parallel
  • bcd44b9 free-threading tests
  • 135747f install twine before checking wheel
  • 69fe0b2 install setuptools before making sdist
  • ee1422b install setuptools before making sdist
  • 5ac2fd2 use windows-11-arm runner to test ARM64 wheel on native env.
  • 36b9745 use windows-11-arm runner to test ARM64 wheel on native env.
  • 4f043ae use auto-mode to make wheel with the native binary.
  • 623196e uses arm docker image to build and test wheel
  • 559fd65 re-enable QEMU to enable arm execution
  • Additional commits viewable in compare view

Updates transformers from 4.31.0 to 5.3.0

Release notes

Sourced from transformers's releases.

v5.1.0: EXAONE-MoE, PP-DocLayoutV3, Youtu-LLM, GLM-OCR

New Model additions

EXAONE-MoE

K-EXAONE is a large-scale multilingual language model developed by LG AI Research. Built using a Mixture-of-Experts architecture, K-EXAONE features 236 billion total parameters, with 23 billion active during inference. Performance evaluations across various benchmarks demonstrate that K-EXAONE excels in reasoning, agentic capabilities, general knowledge, multilingual understanding, and long-context processing.

PP-DocLayoutV3

PP-DocLayoutV3 is a unified and high-efficiency model designed for comprehensive layout analysis. It addresses the challenges of complex physical distortions—such as skewing, curving, and adverse lighting—by integrating instance segmentation and reading order prediction into a single, end-to-end framework.

Youtu-LLM

Youtu-LLM is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks.

GlmOcr

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts.

Breaking changes

  • 🚨 T5Gemma2 model structure (#43633) - Makes sure that the attn implementation is set to all sub-configs. The config.encoder.text_config was not getting its attn set because we aren't passing it to PreTrainedModel.init. We can't change the model structure without breaking so I manually re-added a call to self.adjust_attn_implemetation in modeling code

  • 🚨 Generation cache preparation (#43679) - Refactors cache initialization in generation to ensure sliding window configurations are now properly respected. Previously, some models (like Afmoe) created caches without passing the model config, causing sliding window limits to be ignored. This is breaking because models with sliding window attention will now enforce their window size limits during generation, which may change generation behavior or require adjusting sequence lengths in existing code.

  • 🚨 Delete duplicate code in backbone utils (#43323) - This PR cleans up backbone utilities. Specifically, we have currently 5 different config attr to decide which backbone to load, most of which can be merged into one and seem redundant After this PR, we'll have only one config.backbone_config as a single source of truth. The models will load the backbone from_config and load pretrained weights only if the checkpoint has any weights saved. The overall idea is same as in other composite models. A few config arguments are removed as a result.

  • 🚨 Refactor DETR to updated standards (#41549) - standardizes the DETR model to be closer to other vision models in the library.

  • 🚨Fix floating-point precision in JanusImageProcessor resize (#43187) - replaces an int() with round(), expect light numerical differences

  • 🚨 Remove deprecated AnnotionFormat (#42983) - removes a missnamed class in favour of AnnotationFormat.

... (truncated)

Commits

Updates nltk from 3.8.1 to 3.9.4

Changelog

Sourced from nltk's changelog.

Version 3.10.0 2026-06-11

  • Enforce the stricter nltk.pathsec security policy by default
  • Document the new security model and migration guidance
  • Harden resource loading against path traversal and SSRF/DNS-rebinding
  • Harden downloader path handling and block XML entity expansion
  • Close remaining corpus-reader security edge cases
  • Replace unsafe exec() usage in the utility CLI
  • Warn on unpickling user-provided pickles
  • Add HuggingFace datasets integration (nltk.huggingface)
  • Align TnT with Brants (2000) specifications
  • Fix PorterStemmer irregular-form lowercasing in NLTK mode
  • Fix TransitionParser sparse index dtype for scikit-learn 1.9
  • Fix TextCat tie handling
  • Fix WordNet object comparisons for incompatible types
  • Cache WordNet max depth lazily for lch_similarity()
  • Fix CCG variable direction, substitution, and type-raising bugs
  • Fix Jaro similarity for single-character and empty-string cases
  • Improve CI and release-maintenance workflows

Thanks to the following contributors to 3.10.0: 13rac1, alvations, bowiechen, devesh-2002, ekaf, elias-ba, haosenwang1018, HyperPS, ihitamandal, jancallewaert, jhnwnstd, JuanIMartinezB, Lemm1, LinZiyuu, Mr-Neutr0n, PastelStorm, scruge1, Syzygy2048, ylwango613, yzhaoinuw

Version 3.9.4 2026-03-24

  • Support Python 3.14
  • Fix bug in Levenshtein distance when substitution_cost > 2
  • Fix bug in Treebank detokeniser re quote ordering
  • Fix bug in Jaro similarity for empty strings
  • Several security enhancements
  • Fix GHSA-rf74-v2fm-23pw: unbounded recursion in JSONTaggedDecoder
  • Implement TextTiling vocabulary introduction method (Hearst 1997)
  • Fix ALINE feature matrix errors and add comprehensive tests
  • Support multiple VerbNet versions, fix longid/shortid regex for VerbNet ids
  • Let downloader fallback to md5 when sha256 is unavailable
  • Several other minor bugfixes and code cleanups

Thanks to the following contributors to 3.9.4: Min-Yen Kan, Eric Kafe, Emily Voss, bowiechen, Hrudhai01, jancallewaert, Mr-Neutr0n, pollak.peter89, ylwango613,

Version 3.9.3 2026-02-21

  • Fix CVE-2025-14009: secure ZIP extraction in nltk.downloader (#3468)
  • Block path traversal/arbitrary reads in nltk.data for protocol-less refs (#3467)
  • Block path traversal/abs paths in corpus readers and FS pointers (#3479, #3480)
  • Validate external StanfordSegmenter JARs using SHA256 (#3477)

... (truncated)

Commits
  • ad9c96b Update copyright year
  • 7edcddf Updates for 3.9.4 release
  • 67a2736 Merge pull request #3180 from yzhaoinuw/bug-on-edit_distance_align
  • 2b17ac5 Fix edit_distance_align backtrace for high substitution costs
  • 4b72976 Merge pull request #3018 from JuanIMartinezB/bug/shortid-longid
  • 8a5619f Merge pull request #3222 from Syzygy2048/feature/texttiling-vocabulary-introd...
  • c6574d7 Merge pull request #3289 from ihitamandal/codeflash/optimize-windowdiff-2024-...
  • 98ff5d9 Merge pull request #3435 from Hrudhai01/fix-3260-detokenize-quotes
  • aec4fce Merge pull request #3522 from ekaf/pathsec
  • eec4ee3 Merge pull request #3526 from nltk/update-contributing
  • Additional commits viewable in compare view

Updates sentencepiece from 0.1.99 to 0.2.1

Release notes

Sourced from sentencepiece's releases.

v0.2.1

Major changes

New features

  • [ALL]: Added new build mode to prevent the precompiled normalization rules being embedded in *.so and *.a. (-DSPM_DISABLE_EMBEDDED_DATA=ON). This reduces the runtime size by approximately 1-2 MB. This mode is enabled to build python wheels. The rules are loaded as the data package.

Bug fixes & minor changes

  • [ALL]: Security fix to address a heap overflow issue that could occur when using a model containing an invalid precompiled normalization model.
  • [Python]: Deprecates the wheel package for Linux i686.
  • [Python]: Supported wheel for Windows Arm64. #1114
  • [Python]: Fixed the crash issue on batch decoding #1051
  • [ALL]: Updated the Unicode normalization rule with the latest ICU/Unicode rules.
  • [ALL]: Unused code and build mode cleanup.

v0.2.1pre2

Major changes

New features

  • [ALL]: Added new build mode to prevent the precompiled normalization rules being embedded in *.so and *.a. (-DSPM_DISABLE_EMBEDDED_DATA=ON). This reduces the runtime size by approximately 1-2 MB. This mode is enabled to build python wheels. The rules are loaded as the data package.

Bug fixes & minor changes

  • [ALL]: Security fix to address a heap overflow issue that could occur when using a model containing an invalid precompiled normalization model.
  • [Python]: Deprecates the wheel package for Linux i686.
  • [Python]: Supported wheel for Windows Arm64.
  • [Python]: Fixed the crash issue on batch decoding #1051
  • [ALL]: Updated the Unicode normalization rule with the latest ICU/Unicode rules.
  • [ALL]: Unused code and build mode cleanup.

v0.2.0

Major changes

N/A

New features

  • [ALL] Added SentencePieceNormalizer class in C++/Python. It supports almost the equivalent feature of spm_normalize. Python Sample C++ Sample
  • [ALL] Added SentencePieceProcessor::Normalize method in C++/Python Python Sample C++ Sample
  • [ALL] Added functionality to override the normalization spec before the processing. Python Sample

Bug fixes & minor changes

... (truncated)

Commits
  • 31646a4 Merge pull request #1136 from crusaderky/pytest-run-parallel
  • bcd44b9 free-threading tests
  • 135747f install twine before checking wheel
  • 69fe0b2 install setuptools before making sdist
  • ee1422b install setuptools before making sdist
  • 5ac2fd2 use windows-11-arm runner to test ARM64 wheel on native env.
  • 36b9745 use windows-11-arm runner to test ARM64 wheel on native env.
  • 4f043ae use auto-mode to make wheel with the native binary.
  • 623196e uses arm docker image to build and test wheel
  • 559fd65 re-enable QEMU to enable arm execution
  • Additional commits viewable in compare view

Updates transformers from 4.31.0 to 5.3.0

Release notes

Sourced from transformers's releases.

v5.1.0: EXAONE-MoE, PP-DocLayoutV3, Youtu-LLM, GLM-OCR

New Model additions

EXAONE-MoE

K-EXAONE is a large-scale multilingual language model developed by LG AI Research. Built using a Mixture-of-Experts architecture, K-EXAONE features 236 billion total parameters, with 23 billion active during inference. Performance evaluations across various benchmarks demonstrate that K-EXAONE excels in reasoning, agentic capabilities, general knowledge, multilingual understanding, and long-context processing.

PP-DocLayoutV3

PP-DocLayoutV3 is a unified and high-efficiency model designed for comprehensive layout analysis. It addresses the challenges of complex physical distortions—such as skewing, curving, and adverse lighting—by integrating instance segmentation and reading order prediction into a single, end-to-end framework.

Youtu-LLM

Youtu-LLM is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks.

GlmOcr

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP...

Description has been truncated

@dependabot
Bump the pip group across 3 directories with 5 updates
70872af 
Bumps the pip group with 2 updates in the /src/dependencies/requirements/base_requirements directory: [setuptools](https://github.com/pypa/setuptools) and [torch](https://github.com/pytorch/pytorch).
Bumps the pip group with 1 update in the /src/dependencies/requirements/generated_requirements directory: [torch](https://github.com/pytorch/pytorch).
Bumps the pip group with 3 updates in the /src/maxtext/inference/mlperf directory: [sentencepiece](https://github.com/google/sentencepiece), [transformers](https://github.com/huggingface/transformers) and [nltk](https://github.com/nltk/nltk).


Updates `setuptools` from 78.1.0 to 78.1.1
- [Release notes](https://github.com/pypa/setuptools/releases)
- [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst)
- [Commits](pypa/setuptools@v78.1.0...v78.1.1)

Updates `torch` from 2.11.0 to 2.12.1
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.11.0...v2.12.1)

Updates `setuptools` from 78.1.0 to 78.1.1
- [Release notes](https://github.com/pypa/setuptools/releases)
- [Changelog](https://github.com/pypa/setuptools/blob/main/NEWS.rst)
- [Commits](pypa/setuptools@v78.1.0...v78.1.1)

Updates `torch` from 2.11.0 to 2.12.1
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.11.0...v2.12.1)

Updates `torch` from 2.11.0+cpu to 2.12.1
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.11.0...v2.12.1)

Updates `torch` from 2.11.0+cpu to 2.12.1
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.11.0...v2.12.1)

Updates `sentencepiece` from 0.1.99 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.1.99...v0.2.1)

Updates `transformers` from 4.31.0 to 5.3.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.31.0...v5.3.0)

Updates `nltk` from 3.8.1 to 3.9.4
- [Release notes](https://github.com/nltk/nltk/releases)
- [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog)
- [Commits](nltk/nltk@3.8.1...3.9.4)

Updates `sentencepiece` from 0.1.99 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.1.99...v0.2.1)

Updates `transformers` from 4.31.0 to 5.3.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.31.0...v5.3.0)

Updates `nltk` from 3.8.1 to 3.9.4
- [Release notes](https://github.com/nltk/nltk/releases)
- [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog)
- [Commits](nltk/nltk@3.8.1...3.9.4)

Updates `transformers` from 4.31.0 to 5.3.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.31.0...v5.3.0)

Updates `nltk` from 3.8.1 to 3.9.4
- [Release notes](https://github.com/nltk/nltk/releases)
- [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog)
- [Commits](nltk/nltk@3.8.1...3.9.4)

Updates `sentencepiece` from 0.1.99 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.1.99...v0.2.1)

---
updated-dependencies:
- dependency-name: setuptools
  dependency-version: 78.1.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.12.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: setuptools
  dependency-version: 78.1.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.12.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.12.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.12.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: sentencepiece
  dependency-version: 0.2.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.3.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: nltk
  dependency-version: 3.9.4
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: sentencepiece
  dependency-version: 0.2.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.3.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: nltk
  dependency-version: 3.9.4
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.3.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: nltk
  dependency-version: 3.9.4
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: sentencepiece
  dependency-version: 0.2.1
  dependency-type: direct:production
  dependency-group: pip
...

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@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Jul 1, 2026
@dependabot dependabot Bot added the dependencies Pull requests that update a dependency file label Jul 1, 2026
@dependabot dependabot Bot added the python Pull requests that update python code label Jul 1, 2026
@dependabot dependabot Bot mentioned this pull request Jul 1, 2026
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✅ All modified and coverable lines are covered by tests.

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