Sourced from sentence-transformers's releases.

v5.5.1 - Small Multimodal patch

This patch release fixes a small quirk with multimodal inference when using single-key multimodal inputs like model.encode({"image": ...}).

Install this version with

# Training + Inference
pip install sentence-transformers[train]==5.5.1
Inference only, use one of:
pip install sentence-transformers==5.5.1
pip install sentence-transformers[onnx-gpu]==5.5.1
pip install sentence-transformers[onnx]==5.5.1
pip install sentence-transformers[openvino]==5.5.1
Multimodal dependencies (optional):
pip install sentence-transformers[image]==5.5.1
pip install sentence-transformers[audio]==5.5.1
pip install sentence-transformers[video]==5.5.1
Or combine as needed:
pip install sentence-transformers[train,onnx,image]==5.5.1

Bug fixed

Previously, inference like model.encode({"image": ...}) or model.encode([{"image": ...}, ...]) would be inferred as the ("image",) modality, which differed from the inferred modality of "image" for just model.encode(my_image) or model.encode([my_image, my_image_2, ...]).

This results in confusing errors if the model doesn't have a modality_config mapping for ("image",) in addition to "image", so now a single-key multimodal dict is collapsed to the bare modality (just "image" in this example).

This affected this code:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('BAAI/BGE-VL-base', trust_remote_code=True)
embedding = model.encode({"image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ettin-reranker/mteb_ndcg10_all-MiniLM-L6-v2.png"})
print(embedding.shape)

Which previously failed as the model only implements a path for "text", "image", and ("image", "text").

All Changes

• [fix] Collapse single-key multimodal dicts to bare modality by @​tomaarsen in #3779

Full Changelog: huggingface/sentence-transformers@v5.5.0...v5.5.1

v5.5.0 - Training Agent Skill, EmbedDistillLoss, and ADRMSELoss

This release ships the train-sentence-transformers Agent Skill, adds two new training losses, and brings a long list of robustness and correctness fixes.

The new train-sentence-transformers Agent Skill lets AI coding agents (Claude Code, Codex, Cursor, Gemini CLI, ...) drive end-to-end training and fine-tuning across all three model types. EmbedDistillLoss is a new embedding-level knowledge distillation loss for SentenceTransformer: it aligns a student model's embeddings with pre-computed teacher embeddings, an alternative to the score-based distillation provided by MarginMSELoss and DistillKLDivLoss. ADRMSELoss is a new listwise learning-to-rank loss for CrossEncoder from the Rank-DistiLLM paper. encode() and predict() also gain a per-call processing_kwargs override, and more.

Install this version with

... (truncated)

• ce3ec6d Release v5.5.1
• 610a7c5 [fix] Collapse single-key multimodal dicts to bare modality (#3779)
• f9f3269 Merge branch 'main' into v5.5-release
• 02dc21d Update index tip for v5.5.0 (#3774)
• 833828b Release v5.5.0
• 98ac358 [docs] Load models in float32 in the training examples & docs (#3773)
• d8ee041 [docs] Use modality-neutral terms (input, document) in loss docs & docstrin...
• 4c6850e [examples] Avoid LoggingHandler, silence httpx in examples (#3771)
• 1418823 docs: fix grammar in parallel-sentence-mining README (#3769)
• 22a383d [docs] Use direct class imports in examples & docs (drop `losses.MSELoss(.....
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