[dependabot]

Updates the requirements on sentence-transformers to permit the latest version.

Release notes

Sourced from sentence-transformers's releases.

v6.2.0 - Model merging, faster loading, and distributed evaluation

This release adds model merging for all four model types, reduces redundant Hub requests when loading models, and distributes evaluator inference across DDP ranks. It also includes lower-memory training losses, more flexible multi-vector compression, and fixes throughout inference, training, and evaluation.

Install this version with

# Inference only, use one of:
pip install sentence-transformers==6.2.0
pip install sentence-transformers[openvino]==6.2.0
Multimodal dependencies (optional):
pip install sentence-transformers[image]==6.2.0
pip install sentence-transformers[audio]==6.2.0
pip install sentence-transformers[video]==6.2.0
Training + Inference
pip install sentence-transformers[train]==6.2.0
pip install sentence-transformers[merge]==6.2.0
Or combine as needed:
pip install sentence-transformers[train,image]==6.2.0

Model merging for all four model types (#3776)

Combine compatible fine-tuned checkpoints without another training run using SentenceTransformer.merge, SparseEncoder.merge, CrossEncoder.merge, or MultiVectorEncoder.merge. The integration uses Arcee AI's mergekit and supports well over a dozen methods such as Linear, SLERP, TIES, and DARE.

Install the merge extra with pip install "sentence-transformers[merge]". For example, take a weighted average of two compatible MiniLM models:

from sentence_transformers import SentenceTransformer
models = [
"sentence-transformers/all-MiniLM-L6-v2",
"sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
]
merged = SentenceTransformer.merge(
models,
method="linear",
parameters={"weight": [0.6, 0.4]},
)
merged.save_pretrained("merged-MiniLM-L6")
embeddings = merged.encode(["Combining compatible model weights."])

Or interpolate between the same inputs with SLERP:

</tr></table> 

... (truncated)

Commits

• 0b6937d Release v6.2.0
• 45682ed Update documentation announcement for v6.2 (#4243)
• aed573b Forward gradient checkpointing options to Transformers (#4241)
• c7adf47 Skip bfloat16 transformer head tests on affected Windows runners (#4242)
• 1b4b624 Fix blank rows in CrossEncoder and RRF evaluator CSVs on Windows (#4077)
• ea5435c Treat a string positive or negative as one document in RerankingEvaluator (#4...
• 3a5232b Follow only duplicate pairs in the paraphrase mining transitive closure (#4120)
• 7d64d3b Keep the RRF fusion order the same from run to run (#4067)
• 67f11a3 Evaluate the main similarity function in TripletEvaluator (#4088)
• fd6b188 Accept a string main_score_function in NanoBEIREvaluator (#4034)
• Additional commits viewable in compare view