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Bump the production-dependencies group across 1 directory with 18 updates - #533

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Bumps the production-dependencies group with 16 updates in the / directory:

Package From To
torch 2.11.0 2.13.0
torchcodec 0.11.1 0.15.0
huggingface-hub 1.11.0 1.24.0
transformers 5.5.4 5.14.1
datasets 4.8.4 5.0.0
accelerate 1.13.0 1.14.0
pyannote-audio 4.0.4 4.0.7
pydantic 2.13.3 2.13.4
scikit-learn 1.8.0 1.9.0
nltk 3.9.4 3.10.0
presidio-analyzer 2.2.362 2.2.364
sentence-transformers 5.4.1 5.6.0
av 17.0.1 18.0.0
opencv-python-headless 4.13.0.92 5.0.0.93
ultralytics 8.4.40 8.4.104
notebook-intelligence 2.3.2 5.3.0

Updates torch from 2.11.0 to 2.13.0

Release notes

Sourced from torch's releases.

PyTorch 2.13.0 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.

Tracked Regressions

ROCm wheels break torch.compile on CPU in environments without a GPU

Running a torch==2.13.0+rocm7.2 wheel in an environment where no GPU is available (torch.cuda.is_available() is False) breaks torch.compile on the CPU path: the first compile raises RuntimeError: Can't detect vectorized ISA for CPU (#189194). This is a regression from torch==2.12.1+rocm7.2, which compiles CPU code fine (detecting e.g. VecAVX2) in the same setup. The 2.13 ROCm wheel appears to rely on something present in the ROCm builder image to detect the CPU vectorized ISA, so it works when run on a ROCm image but fails on a plain CPU-only image.

Workaround: run the +rocm wheel on a ROCm image, or install a standard CPU/CUDA build for GPU-less environments.

Backwards Incompatible Changes

  • Stop building CPython 3.13t (free-threaded) binaries (#182951)

    Upstream pypa/manylinux removed CPython 3.13t (free-threaded) on 2026-05-07, because 3.13t was experimental and has been superseded by the now-non-experimental CPython 3.14t. As a result, PyTorch 2.13 no longer ships cp313t wheels (Linux, Triton, and related artifacts). Users on the free-threaded interpreter should move to Python 3.14t.

    PyTorch 2.12:

    # cp313t (free-threaded 3.13) wheels were available
python3.13t -m pip install torch

    PyTorch 2.13:

... (truncated)

Commits
  • cf30153 [release/2.13] Strip +PTX from CUDA arch list on release/RC builds (#188914) ...
  • 3e3e24b [release/2.13] Restrict cuda-bindings to Python < 3.15 for CUDA 12.9 builds (...
  • 7986b06 [release/2.13] Bump binary build timeout 280 -> 400 minutes (#188551)
  • 0bdbc26 [release/2.13] Add CUDA 12.9 to TORCH_CUDA_ARCH_LIST tables (#188443)
  • 9cabb45 [release/2.13] Update manywheel docker image pin to 78e737ad (#188409)
  • 78e737a [release/2.13] Revert "Tighten generalized scatter graph target (#184075)" (#...
  • 0bb9b5b [release/2.13] Revert "dynamo: round-trip torch.cuda.stream ctx mgr across gr...
  • aaac2bf [release/2.13] Revert "[Reland] Port D104346887/PR 182675 for index_add fast ...
  • 9330813 Fix build_with_debinfo.py broken by CONFIGURE_DEPENDS globbing (#188192)
  • 4e077a7 Remove setuptools upper bound (#188190)
  • Additional commits viewable in compare view

Updates torchvision from 0.26.0 to 0.28.0

Release notes

Sourced from torchvision's releases.

TorchVision 0.28.0 Release

TorchVision 0.28 is out with some small enhancement and bug-fixes:

Enhancements

  • [transforms] Let wrap() preserve metadata for custom TVTensor subclasses (#9490)
  • [transforms] Allow strings for interpolation param in resize transforms (#9461)

Bug fixes

  • [transforms] Fix F.resize on tv_tensors.Mask to honor NEAREST_EXACT interpolation. Previously the interpolation argument was ignored for mask inputs (resize_mask hardcoded NEAREST), so NEAREST_EXACT silently produced plain NEAREST output (#9497)
  • [io] Fix a GIF decoder bug on malformed GIFs that could write outside the allocated tensor's memory (#9520)

Contributors

🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Andrey Talman, Benson Ma, Jason Fried, Joanne Yun, Nicolas Hug

TorchVision 0.27.1 Release

This is a patch release, which is compatible with PyTorch 2.12.1. There are no new features added.

TorchVision 0.27 Release

TorchVision 0.27 is out! This is a small release where the main improvement is the addition of the popular lanczos interpolation mode for the v2.Resize transform on CPU. Results are equivalent to PIL's, but you can expect TorchVision to be faster as it leverages AVX2 (on x86) and NEON paths (on ARM).

Improvements

[transforms] Add support for lanczos interpolation mode (#9459) [transforms] Drastically speed-up Resize on NEON ARM (#9439) [ops] Vectorize masks_to_boxes for performance (#9358) [ops, transforms] Add direct XYWH-CXCYWH conversion for better performance (#9326) [datasets] torchvision.datasets.voc: update dataset and project site URLs (#9216) [ops] Add support for rotated boxes in box_iou (#9404, #9379) [ops][MPS] Improve runtime complexity of roi_align (#9100) [Code quality] #9359, #9364, #9359, #9317, #9409, #9408, #9416, #9411, #4463, #9475, #9427, #9448, #9443, #9396, #9316, #9286, #9324, #9338, #9381, #9386

[Documentation] #9339, #9351, #9323, #9374, #9412, #9378, #9428, #9431, #9474, #9472, #9463, #9440, #9385, #9327, #9334, #8879, #9350, #9392

Bug Fixes

[transforms] Fix incorrect normalization axis in v2.ElasticTransform (#9300) [transforms] Fix: add clamping to avoid v2.ElasticTransform IndexError when bbox equals canvas size (#9436) [transforms] Fix tv_tensors.wrap to preserve subclass types for BoundingBoxes and KeyPoints (#9332) [transforms] Fix CXCYWH to XYXY conversion for integer bounding boxes (#9322) [ops] Fix masks_to_boxes for empty masks (#9357)

Contributors

... (truncated)

Commits

Updates torchcodec from 0.11.1 to 0.15.0

Release notes

Sourced from torchcodec's releases.

TorchCodec 0.15 is out! This is a small release compatible with torch >= 2.11, with the following improvements:

  • meta-pytorch/torchcodec#1503 and meta-pytorch/torchcodec#1504 improved decoding coverage on some videos, where a premature "end of file" would otherwise be raised.
  • meta-pytorch/torchcodec#1489 optimizes forward seeks - you should see better performance on sparse decoding scenarios, especially on CPU when num_ffmpeg_threads is high.
  • Free-threaded wheels are now shipped for MacOS (and Linux, but that was already supported).

TorchCodec 0.14: HDR Video Decoding for CPU & CUDA, and Fast Wav Decoder

TorchCodec 0.14 is out! It is compatible with torch >= 2.11. It comes with two major additions: a fast audio WavDecoder, and support for HDR video decoding!

Fast wav decoder

Inspired by SDPL's fast wav decoder, TorchCodec now has a dedicated WavDecoder for decoding WAV files. It bypasses FFmpeg entirely and reads WAV data directly, resulting in significantly faster decoding. It supports multiple sample formats (int16, int32, float32, etc.), and can decode from files, bytes, or file-like objects.

from torchcodec.decoders import WavDecoder
decoder = WavDecoder("audio.wav")
samples = decoder.get_all_samples()  # AudioSamples with data and sample_rate

Read more in our docs.

HDR Video Decoding

VideoDecoder now supports HDR (High Dynamic Range) video decoding without losing precision. When output_dtype=torch.float32 is specified, the decoder outputs RGB float32 frames in [0, 1], preserving the full HDR color range. This is supported for both CPU and CUDA!

import torch
from torchcodec.decoders import VideoDecoder
decoder = VideoDecoder("hdr_video.mp4", output_dtype=torch.float32)
frame = decoder[0]  # Full HDR precision in float32

Read more in our docs.

⚠️ This feature is in beta stage, so behavior may slightly change depending on user feedback. Let us know if you encounter any issue!

Other Improvements

  • Improved audio seeking: AudioDecoder seeking is now much faster (#1449)
  • Dropped NPP dependency: TorchCodec no longer depends on NVIDIA's NPP library, which will simplify installing and using TorchCodec for CUDA decoding.

Bug Fixes

  • Fix a rare crash scenario during process teardown with the CUDA decoder (#1441)
  • Fix CUDA decoding of videos with odd dimensions(#1462)

TorchCodec 0.13

TorchCodec 0.13 is out! It is compatible with torch >= 2.11, and it is packed with new features.

... (truncated)

Commits

Updates huggingface-hub from 1.11.0 to 1.24.0

Release notes

Sourced from huggingface-hub's releases.

[v1.24.0] Name your Jobs! (and download fixes)

📊 Name your Jobs!

Jobs on the Hub now support an optional --name flag on the CLI and a name parameter on the Python API (run_job, run_uv_job, create_scheduled_job, create_scheduled_uv_job). Names are stored as the name label and make Jobs easier to find and identify in the UI. You can also name an existing Job using hf jobs labels <job_id> --name my-job. Names are optional and do not need to be unique.

# Create a named Job
hf jobs run --name training-v2 python:3.12 python train.py
Name an existing Job
hf jobs labels <job_id> --name training-v2
Named scheduled Job
hf jobs scheduled run @​hourly --name hourly-task python:3.12 python -c 'print("This runs every hour!")'

📚 Documentation: CLI guide, Jobs guide

📖 Documentation

The README has been completely refreshed to put the hf CLI first. The standalone installer (curl/PowerShell) and a terminal quick start — covering auth login, models ls, download, upload, and jobs run — now appear before the Python library section. A new For AI agents section introduces hf skills add for Codex, Cursor, OpenCode, Claude Code, and other AI tools. The Python content remains intact under the renamed Use the Python library heading, with refreshed example models and a corrected tagline ("The official CLI and Python client for the Hugging Face Hub").

🐛 Bug and typo fixes

  • [Download] Fix xet download rate: show summed, not per-file speed by @​rajatarya in #4530

🏗️ Internal

[v1.23.0] Space templates, CLI extension updates & smoother Xet downloads

🚀 Create Spaces from templates

You can now seed a new Space from one of the official Hub templates (JupyterLab, a Gradio chatbot, a Streamlit app, etc.) instead of starting from an empty repo. List what's available with the new list_space_templates() API or the hf spaces templates CLI command, then pass a template's repo_id (or its short name) to create_repo(..., space_template=...) or hf repos create --type space --template. The Space SDK is inferred from the template, and templates recommended as private (like JupyterLab) are created privately by default unless you explicitly choose a visibility.

# List available templates
$ hf spaces templates
NAME        REPO_ID                             SDK     PREFERRED_PRIVATE
----------- ----------------------------------- ------- -----------------
Streamlit   streamlit/streamlit-template-space  docker
JupyterLab  SpacesExamples/jupyterlab           docker  ✔
</tr></table>

... (truncated)

Commits
  • 36fd32c Release: v1.24.0
  • 240dcf4 Release: v1.24.0.rc3
  • d43b771 Release: v1.24.0.rc2
  • 16e19c2 Release: v1.24.0.rc1
  • 2917e60 Release: v1.24.0.rc0
  • b2ae5b6 [Jobs] Add optional names to Jobs CLI and API (#4532)
  • dc5c540 CLI-first README: standalone installer and terminal quick start up top (#4527)
  • 74082b7 [Download] Fix xet download rate showing per-file speed instead of summed tot...
  • 91dc59a Bump the actions group with 4 updates (#4503)
  • 8a3189e [Docs] Fix HTTP client name in download docs (#4523)
  • Additional commits viewable in compare view

Updates transformers from 5.5.4 to 5.14.1

Release notes

Sourced from transformers's releases.

Patch release: v5.14.1

Patch release v5.14.1

This patch solves a few issues which appeared when integrating Inkling model, most notably an issue affecting models using EncoderDecoderCache during assisted generation. It also fixes an issue that could appear during prefill with StaticCache and sdpa without padding for Inkling which uses a position_bias. It contains the following commits:

Release v5.14.0

New Model additions

Inkling (fresh from Thinking Machines): 975B total, 41B active

Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI- powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.

TIPSv2

Links: Documentation

TIPSv2 DPT

Links: Documentation

🚨 Breaking changes

... (truncated)

Commits

Updates datasets from 4.8.4 to 5.0.0

Release notes

Sourced from datasets's releases.

5.0.0

Datasets Features

Agent traces

  • Parse Agent traces messages for SFT using teich by @​lhoestq in huggingface/datasets#8232

    • Agent traces from claude_code/pi/codex and others can now be loaded with load_dataset
    • Using the teich library (new optional dependency), traces are parsed to messages to enable training on traces using e.g. trl
    • Load the data:
    >>> from datasets import load_dataset
>>> ds = load_dataset("lhoestq/agent-traces-example", split="train")
>>> ds[0]["messages"]
[{'role': 'user', 'content': 'Download a random dataset from Hugging Face, use DuckDB to inspect it, and come back with a short report about it. Be concise and include: dataset name, what files/format you found, row count or rough size if you can determine it,...'
 ...]
    • Train on agent traces:
    trl sft --dataset-name lhoestq/agent-traces-example ...

Next-level shuffling in streaming mode

  • Use multiple input shards for shuffle buffer by @​lhoestq in huggingface/datasets#8194

    ds = load_dataset(..., streaming=True)
ds = ds.shuffle(seed=42)
# or configure local buffer shuffling manually, default is:
ds = ds.shuffle(seed=42, buffer_size=1000, max_buffer_input_shards=10)

    before👎:

    after✨:

    toy example comparison

    from datasets import IterableDataset
ds = IterableDataset.from_dict({"i": range(123_456_789)}, num_shards=1024)
ds = ds.shuffle(seed=42)
print("Cold start ids:")

... (truncated)

Commits
  • 68ac1a9 Release: 5.0.0 (#8239)
  • cfe4492 Support composed splits in streaming datasets (#8220)
  • fd67320 Keep None as a real null in Json() columns instead of the string "null" (#8231)
  • 10cdc81 Fix iterable skip over full Arrow blocks (#8236)
  • b7c064d Parse agent traces messages for SFT using teich (#8232)
  • 31e92f1 fix: embed_external_files=True for mesh support (#8224)
  • d168d5f feat: add TsFile (Apache IoTDB) packaged builder with per-device wide format ...
  • 992f3cf fix(map): fix progress bar exceeding total when load_from_cache_file=False (#...
  • 8474a91 Fix single lance file form pylance 7.0 (#8225)
  • d4284e9 feat: add 3D mesh support and MeshFolder builder (#8055)
  • Additional commits viewable in compare view

Updates accelerate from 1.13.0 to 1.14.0

Release notes

Sourced from accelerate's releases.

v1.14.0: AMD ROCm support, FSDP2 hardening

FSDP2 Improvements

This release brings a large batch of FSDP2 fixes and quality-of-life improvements: correct dtype handling on load, sharding of embeddings/norms, QLoRA crash prevention, and a more robust auto-wrap policy.

  • Fsdp2 fully_shard embedding and norm by @​SunMarc in #4015
  • Fix fsdp2 load full state dict dtype mismatch by @​SunMarc in #4021
  • Fix region compilation fsdpv2 by @​SunMarc in #4022
  • [FSDP2] Cast model to uniform dtype before fully_shard to fix mixed-dtype AssertionError by @​roycho96 in #3985
  • [FSDP2] Auto-exclude non-floating frozen Params4bit from fully_shard to prevent QLoRA crash by @​roycho96 in #3987
  • fix(FSDP2): auto-wrap policy ignoring _no_split_modules fallback by @​JohnGiorgi in #3999
  • fix: use key-based matching in fsdp2_load_full_state_dict by @​roycho96 in #3982
  • fix: add missing model_has_params4bit guard to fsdp2_load_full_state_dict call by @​roycho96 in #3981
  • Fix to-fsdp2: drop REMOVED / NOT_YET_IMPLEMENTED FSDP1 keys instead of leaking them by @​lollinng in #4065
  • Prevent double-wrapping models in prepare_model() by @​joshuaswanson in #3977

AMD ROCm support

Accelerate now works end-to-end on AMD ROCm devices. Thanks @​Abdennacer-Badaoui!

Neuron

Further Neuron improvements to reduce recompilation and cover missing device cases.

Quantization & Offloading

We improved offloading support for quantized models, including Torchao, int8, and tied-weight handling.

Data Loading

  • Feat: Support dynamic batch size in BatchSamplerShard with even_batches by @​yuxinyuan in #3969
  • Fix iterable dataset sharding condition when n_shards == num_processes by @​SunMarc in #3958
  • Fix implicit padding in split_between_processes when apply_padding=False and num_samples < num_processes by @​3manifold in #4052

Minor fixes

... (truncated)

Commits
  • beb0672 Release: v1.14.0
  • a5cb37c Fix all tests (#4072)
  • 14d7cdc Support dynamic batch size in BatchSamplerShard with even_batches (#3969)
  • 77eb5cf fix(tracker): guard init_trackers and log against None kwargs (#4026)
  • 379c8e0 fix(security): remediate workflow vulnerability in .github/workflows/build-do...
  • 61e6420 Fix to-fsdp2: actually drop REMOVED / NOT_YET_IMPLEMENTED FSDP1 keys (#4065)
  • b120c1f tests: Gracefully handle missing set_device for mps (#4028)
  • 418015a test: add regression test for no_split_module_classes accepting set type (#4048)
  • 3f1acf8 Bump the actions group with 8 updates (#4068)
  • a2e5292 docs: add aggregate profiler memory example (#4054)
  • Additional commits viewable in compare view

Updates pyannote-audio from 4.0.4 to 4.0.7

Changelog

Sourced from pyannote-audio's changelog.

Version 4.0.7 (2026-06-30)

  • fix(pipeline): add missing support for subfolder in Pipeline.from_pretrained
  • feat(pipeline): add basic support for (sequential) batch inference

Version 4.0.5 (2026-06-22)

  • improve(telemetry): reduce number of sent packages @​litdarya
  • feat(cli): add --average-case option to optimize command @​antoinelaurent
  • fix(task): fix Task.prepare_data to support saving preprocessors that produce int values in metadata @​lylyhan
Commits

Updates pydantic from 2.13.3 to 2.13.4

Release notes

Sourced from pydantic's releases.

v2.13.4 2026-05-06

v2.13.4 (2026-05-06)

What's Changed

Packaging

Fixes

Full Changelog: pydantic/pydantic@v2.13.3...v2.13.4

Changelog

Sourced from pydantic's changelog.

v2.13.4 (2026-05-06)

GitHub release

What's Changed

Packaging

Fixes

Commits
  • cf67d4b Fix linting
  • f0d8a21 Prepare release v2.13.4
  • 5e3fe1d Check for pydantic tag pattern in CI
  • 7f9edcc Document tagging conventions
  • b46a0c9 Adapt pydantic-core linker flags on macOS
  • 50629c8 Update to PyPy 7.3.22
  • 8522ebb Preserve RootModel core metadata
  • a37f3af Adapt MISSING sentinel test to work with unreleased typing_extensions ver...
  • 909259a Remove Logfire example in documentation
  • 2c4174c Bump libc from 0.2.155 to 0.2.185
  • See full diff in compare view

Updates scikit-learn from 1.8.0 to 1.9.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.9.0

We're happy to announce the 1.9.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_9_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.9.html

This release adds narwhals as a new dependency that will help to improve dataframe interoperability across the project.

This version supports Python versions 3.11 to 3.14.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn
Commits
  • 77def0e trigger wheel builder [cd build]
  • ee7c0b0 generate changelog
  • 3d7fb04 bump version
  • 8954e7b DOC Release highlights for 1.9 (#34147)
  • 73a3eab Fix: Array-API - avoid failing for numpy fit + predict with sparse or array-l...
  • 8839aae DOC Thread-safety requirement for open_listener message consumer callback (#3...
  • 4d2476a DOC Refactor array API docs page (#34054)
  • f9f812f 🔒 🤖 CI Update lock files for scipy-dev CI build(s) 🔒 🤖 ...
  • d779dc3 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
  • 6a03cf0 🔒 🤖 CI Update lock files for array-api CI build(s) 🔒 🤖 ...
  • Additional commits viewable in compare view

Updates nltk from 3.9.4 to 3.10.0

Release notes

Sourced from nltk's releases.

v3.10.0-rc1

What's Changed

@dependabot
Bump the production-dependencies group across 1 directory with 18 upd…
b01561e 
…ates

Bumps the production-dependencies group with 16 updates in the / directory:

| Package | From | To |
| --- | --- | --- |
| [torch](https://github.com/pytorch/pytorch) | `2.11.0` | `2.13.0` |
| [torchcodec](https://github.com/pytorch/torchcodec) | `0.11.1` | `0.15.0` |
| [huggingface-hub](https://github.com/huggingface/huggingface_hub) | `1.11.0` | `1.24.0` |
| [transformers](https://github.com/huggingface/transformers) | `5.5.4` | `5.14.1` |
| [datasets](https://github.com/huggingface/datasets) | `4.8.4` | `5.0.0` |
| [accelerate](https://github.com/huggingface/accelerate) | `1.13.0` | `1.14.0` |
| [pyannote-audio](https://github.com/pyannote/pyannote-audio) | `4.0.4` | `4.0.7` |
| [pydantic](https://github.com/pydantic/pydantic) | `2.13.3` | `2.13.4` |
| [scikit-learn](https://github.com/scikit-learn/scikit-learn) | `1.8.0` | `1.9.0` |
| [nltk](https://github.com/nltk/nltk) | `3.9.4` | `3.10.0` |
| [presidio-analyzer](https://github.com/data-privacy-stack/presidio) | `2.2.362` | `2.2.364` |
| [sentence-transformers](https://github.com/huggingface/sentence-transformers) | `5.4.1` | `5.6.0` |
| [av](https://github.com/PyAV-Org/PyAV) | `17.0.1` | `18.0.0` |
| [opencv-python-headless](https://github.com/opencv/opencv-python) | `4.13.0.92` | `5.0.0.93` |
| [ultralytics](https://github.com/ultralytics/ultralytics) | `8.4.40` | `8.4.104` |
| [notebook-intelligence](https://github.com/plmbr/notebook-intelligence) | `2.3.2` | `5.3.0` |



Updates `torch` from 2.11.0 to 2.13.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.11.0...v2.13.0)

Updates `torchvision` from 0.26.0 to 0.28.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.26.0...v0.28.0)

Updates `torchcodec` from 0.11.1 to 0.15.0
- [Release notes](https://github.com/pytorch/torchcodec/releases)
- [Commits](meta-pytorch/torchcodec@v0.11.1...v0.15.0)

Updates `huggingface-hub` from 1.11.0 to 1.24.0
- [Release notes](https://github.com/huggingface/huggingface_hub/releases)
- [Commits](huggingface/huggingface_hub@v1.11.0...v1.24.0)

Updates `transformers` from 5.5.4 to 5.14.1
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v5.5.4...v5.14.1)

Updates `datasets` from 4.8.4 to 5.0.0
- [Release notes](https://github.com/huggingface/datasets/releases)
- [Commits](huggingface/datasets@4.8.4...5.0.0)

Updates `accelerate` from 1.13.0 to 1.14.0
- [Release notes](https://github.com/huggingface/accelerate/releases)
- [Commits](huggingface/accelerate@v1.13.0...v1.14.0)

Updates `pyannote-audio` from 4.0.4 to 4.0.7
- [Release notes](https://github.com/pyannote/pyannote-audio/releases)
- [Changelog](https://github.com/pyannote/pyannote-audio/blob/main/CHANGELOG.md)
- [Commits](pyannote/pyannote-audio@4.0.4...4.0.7)

Updates `pydantic` from 2.13.3 to 2.13.4
- [Release notes](https://github.com/pydantic/pydantic/releases)
- [Changelog](https://github.com/pydantic/pydantic/blob/main/HISTORY.md)
- [Commits](pydantic/pydantic@v2.13.3...v2.13.4)

Updates `scikit-learn` from 1.8.0 to 1.9.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.8.0...1.9.0)

Updates `nltk` from 3.9.4 to 3.10.0
- [Release notes](https://github.com/nltk/nltk/releases)
- [Changelog](https://github.com/nltk/nltk/blob/develop/ChangeLog)
- [Commits](nltk/nltk@3.9.4...v3.10.0)

Updates `presidio-analyzer` from 2.2.362 to 2.2.364
- [Release notes](https://github.com/data-privacy-stack/presidio/releases)
- [Changelog](https://github.com/data-privacy-stack/presidio/blob/main/CHANGELOG.md)
- [Commits](data-privacy-stack/presidio@2.2.362...2.2.364)

Updates `spacy` from 3.8.14 to 3.8.13
- [Release notes](https://github.com/explosion/spaCy/releases)
- [Commits](explosion/spaCy@release-v3.8.14...release-v3.8.13)

Updates `sentence-transformers` from 5.4.1 to 5.6.0
- [Release notes](https://github.com/huggingface/sentence-transformers/releases)
- [Commits](huggingface/sentence-transformers@v5.4.1...v5.6.0)

Updates `av` from 17.0.1 to 18.0.0
- [Release notes](https://github.com/PyAV-Org/PyAV/releases)
- [Changelog](https://github.com/PyAV-Org/PyAV/blob/main/CHANGELOG.rst)
- [Commits](PyAV-Org/PyAV@v17.0.1...v18.0.0)

Updates `opencv-python-headless` from 4.13.0.92 to 5.0.0.93
- [Release notes](https://github.com/opencv/opencv-python/releases)
- [Commits](https://github.com/opencv/opencv-python/commits)

Updates `ultralytics` from 8.4.40 to 8.4.104
- [Release notes](https://github.com/ultralytics/ultralytics/releases)
- [Commits](ultralytics/ultralytics@v8.4.40...v8.4.104)

Updates `notebook-intelligence` from 2.3.2 to 5.3.0
- [Release notes](https://github.com/plmbr/notebook-intelligence/releases)
- [Changelog](https://github.com/plmbr/notebook-intelligence/blob/main/CHANGELOG.md)
- [Commits](plmbr/notebook-intelligence@v2.3.2...v5.3.0)

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.13.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: torchvision
  dependency-version: 0.28.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: torchcodec
  dependency-version: 0.15.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: huggingface-hub
  dependency-version: 1.24.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: transformers
  dependency-version: 5.14.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: datasets
  dependency-version: 5.0.0
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: production-dependencies
- dependency-name: accelerate
  dependency-version: 1.14.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: pyannote-audio
  dependency-version: 4.0.7
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: production-dependencies
- dependency-name: pydantic
  dependency-version: 2.13.4
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: production-dependencies
- dependency-name: scikit-learn
  dependency-version: 1.9.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: nltk
  dependency-version: 3.10.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: presidio-analyzer
  dependency-version: 2.2.364
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: production-dependencies
- dependency-name: spacy
  dependency-version: 3.8.13
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: production-dependencies
- dependency-name: sentence-transformers
  dependency-version: 5.6.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: production-dependencies
- dependency-name: av
  dependency-version: 18.0.0
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: production-dependencies
- dependency-name: opencv-python-headless
  dependency-version: 5.0.0.93
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: production-dependencies
- dependency-name: ultralytics
  dependency-version: 8.4.104
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: production-dependencies
- dependency-name: notebook-intelligence
  dependency-version: 5.3.0
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: production-dependencies
...

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@dependabot dependabot Bot added dependencies Pull requests that update a dependency file major python:uv Pull requests that update python:uv code labels Jul 22, 2026
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🚀 First Pull Request 🎉
Welcome to senselab, and thank you for submitting your first pull request!
We’re thrilled to have your contribution. Our team will review it as soon as possible.
Stay engaged, and let’s make behavioral data analysis even more powerful together!

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