Bump sentence-transformers from 5.3.0 to 5.4.0

[dependabot]

Bumps sentence-transformers from 5.3.0 to 5.4.0.

Release notes

Sourced from sentence-transformers's releases.

v5.4.0 - Multimodal Embeddings and Reranking, Modular CrossEncoder, Flash Attention Input Flattening

This large release introduces first-class multimodal support for both SentenceTransformer and CrossEncoder, making it easy to compute embeddings and rerank across text, images, audio, and video. The CrossEncoder class has been fully modularized, allowing for generative rerankers (CausalLM-based models) via a new LogitScore module. Flash Attention 2 now automatically skips padding for text-only inputs, providing significant speedups & memory reductions, especially when input lengths vary.

Blog post: Multimodal Embedding & Reranker Models with Sentence Transformers: a walkthrough of the new multimodal capabilities with some practical examples.

Migration guide: Migrating from v5.x to v5.4+: covers updated import paths, renamed parameters, and other softly breaking changes with deprecation warnings. Note that there are no hard deprecations, all existing code should continue to work with warnings at worst.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==5.4.0
Inference only, use one of:
pip install sentence-transformers==5.4.0
pip install sentence-transformers[onnx-gpu]==5.4.0
pip install sentence-transformers[onnx]==5.4.0
pip install sentence-transformers[openvino]==5.4.0
Multimodal dependencies (optional):
pip install sentence-transformers[image]==5.4.0
pip install sentence-transformers[audio]==5.4.0
pip install sentence-transformers[video]==5.4.0
Or combine as needed:
pip install sentence-transformers[train,onnx,image]==5.4.0

Multimodal Embeddings with SentenceTransformer (#3554)

SentenceTransformer now natively supports vision-language models (VLMs) and other multimodal architectures. You can encode and compare across text, images, audio, videos, or combinations of these, with automatic modality detection and preprocessing. Models advertise which modalities they support via the new model.modalities property and model.supports() method.

Using a pretrained multimodal embedding model

from PIL import Image
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
"Qwen/Qwen3-VL-Embedding-2B",
model_kwargs={"attn_implementation": "flash_attention_2", "torch_dtype": "bfloat16"},
processor_kwargs={"min_pixels": 28 * 28, "max_pixels": 600 * 600},
revision="refs/pr/23",
)
Check supported modalities
print(model.modalities)
['text', 'image', 'video', 'message']
print(model.supports("image"))
True
</tr></table>

... (truncated)

Commits

• fe93612 Merge branch 'main' into v5.4-release
• 29408d5 Add model_card_templates to MANIFEST.in to package them nicely (#3709)
• cec69df Release v5.4.0
• 95cd011 Update index.rst tip/note about last release (#3708)
• afebd45 Extend the migration guide for completeness (#3707)
• 7910e90 Add Flash Attention 2 input flattening benchmark to efficiency docs (#3706)
• 802317b fix: Separate tokenize and forward kwargs in SparseEncoder.encode to prevent ...
• 2ebf4f6 Jina-reranker-m0 doesn't require a revision anymore (#3705)
• 0e0da53 Merge pull request #3704 from tomaarsen/fix/triplet_distance
• 2b232af [v5.4] Introduce cross-modality and multi-modality support; modularize Cros...
• Additional commits viewable in compare view

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