Sourced from sentence-transformers's releases.

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

# Training + Inference
pip install sentence-transformers[train]==5.5.0
Inference only, use one of:
pip install sentence-transformers==5.5.0
pip install sentence-transformers[onnx-gpu]==5.5.0
pip install sentence-transformers[onnx]==5.5.0
pip install sentence-transformers[openvino]==5.5.0
Multimodal dependencies (optional):
pip install sentence-transformers[image]==5.5.0
pip install sentence-transformers[audio]==5.5.0
pip install sentence-transformers[video]==5.5.0
Or combine as needed:
pip install sentence-transformers[train,onnx,image]==5.5.0

The train-sentence-transformers Agent Skill (#3752)

If you use an AI coding agent (Claude Code, Codex, Cursor, Gemini CLI, OpenCode, ...), you can now install the train-sentence-transformers Agent Skill and ask your agent to fine-tune a model on your data:

hf skills add train-sentence-transformers              # installs under ./.agents/skills/
hf skills add train-sentence-transformers --global     # installs under ~/.agents/skills/
hf skills add train-sentence-transformers --claude     # also symlinks into .claude/skills/

The skill gives the agent curated, version-aware guidance for training SentenceTransformer (bi-encoder), CrossEncoder (reranker), and SparseEncoder/SPLADE models, covering base model selection, loss and evaluator choice, hard-negative mining, distillation, LoRA, Matryoshka, multilingual training, static embeddings, plus a set of production-ready training template scripts. Then you can prompt your agent with things like:

"Train a multilingual sentence-transformer on Dutch legal pairs."

"Fine-tune a cross-encoder reranker on (question, answer) pairs from my dataset, mine hard negatives, and push to my Hub repo."

"Train a German sparse embedding model with high sparsity."

"Can you train a static embedding model on 100k code triplets?"

The skill lives in the repository under skills/train-sentence-transformers/ and is mirrored to the huggingface/skills marketplace on each release.

New loss: EmbedDistillLoss (#3665)

... (truncated)

• 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(.....
• 68e4d61 [fix] Unwrap DDP/torch.compile wrappers in AdaptiveLayerLoss (#3768)
• 8151750 [fix] Use first non-pad token for CLS pooling with left-padding (#3767)
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