chore(deps): bump sentence-transformers from 5.0.0 to 5.1.0 in /backend/python/transformers

[dependabot]

Bumps sentence-transformers from 5.0.0 to 5.1.0.

Release notes

Sourced from sentence-transformers's releases.

v5.1.0 - ONNX and OpenVINO backends offering 2-3x speedups; more hard negatives mining formats

This release introduces 2 new efficient computing backends for SparseEncoder embedding models: ONNX and OpenVINO + optimization & quantization, allowing for speedups up to 2x-3x; a new "n-tuple-score" output format for hard negative mining for distillation; gathering across devices for free lunch on multi-gpu training; trackio support; MTEB documentation; any many small fixes and features.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==5.1.0
Inference only, use one of:
pip install sentence-transformers==5.1.0
pip install sentence-transformers[onnx-gpu]==5.1.0
pip install sentence-transformers[onnx]==5.1.0
pip install sentence-transformers[openvino]==5.1.0

Faster ONNX and OpenVINO backends for SparseEncoder models (#3475)

Introducing a new backend keyword argument to the SparseEncoder initialization, allowing values of "torch" (default), "onnx", and "openvino". These require installing sentence-transformers with specific extras:

pip install sentence-transformers[onnx-gpu]
# or ONNX for CPU only:
pip install sentence-transformers[onnx]
# or
pip install sentence-transformers[openvino]

It's as simple as:

from sentence_transformers import SparseEncoder
Load a SparseEncoder model with the ONNX backend
model = SparseEncoder("naver/splade-v3", backend="onnx")
query = "Which planet is known as the Red Planet?"
documents = [
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]
query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
torch.Size([30522]) torch.Size([4, 30522])
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
tensor([[12.1450, 26.1040, 22.0025, 23.3877]])
</tr></table>

... (truncated)

Commits

• a5e0425 Release v5.1.0
• 2cf753f Update main sbert.net page with v5.1 mention (#3482)
• 58622d4 [feat] Allow n-tuples for CE MarginMSE training (#3481)
• 6e7d64e docs: add MTEB evaluation guide and update usage.rst (#3477)
• 08446fc Use SHA256 with usedforsecurity=False in hard negatives caching (#3479)
• 55f1dd0 chore: Handle error when predict is called with an empty sentence list (#3466)
• 99dda77 [feat] Add ONNX, OV support for SparseEncoder; refactor ONNX/OV (#3475)
• 69406a3 [docs] Add splade_index semantic search example (#3473)
• 240cf30 docs: Fix dead link in ContrastiveLoss references (#3476)
• 7e8d2cc Fix: prevent loading best model when PEFT adapters are active (#3470)
• Additional commits viewable in compare view

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