chore(deps): Bump sentence-transformers from 3.1.1 to 3.2.0 in /backend/python/sentencetransformers

[dependabot]

Bumps sentence-transformers from 3.1.1 to 3.2.0.

Release notes

Sourced from sentence-transformers's releases.

v3.2.0 - ONNX and OpenVINO backends offering 2-3x speedup; Static Embeddings offering 50x-500x speedups at ~10-20% performance cost

This release introduces 2 new efficient computing backends for SentenceTransformer models: ONNX and OpenVINO + optimization & quantization, allowing for speedups up to 2x-3x; static embeddings via Model2Vec allowing for lightning-fast models (i.e., 50x-500x speedups) at a ~10%-20% performance cost; and various small improvements and fixes.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==3.2.0
Inference only, use one of:
pip install sentence-transformers==3.2.0
pip install sentence-transformers[onnx-gpu]==3.2.0
pip install sentence-transformers[onnx]==3.2.0
pip install sentence-transformers[openvino]==3.2.0

Faster ONNX and OpenVINO Backends for SentenceTransformer (#2712)

Introducing a new backend keyword argument to the SentenceTransformer initialization, allowing values of "torch" (default), "onnx", and "openvino". These come with new installations:

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 SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2", backend="onnx")
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)

If you specify a backend and your model repository or directory contains an ONNX/OpenVINO model file, it will automatically be used! And if your model repository or directory doesn't have one already, an ONNX/OpenVINO model will be automatically exported. Just remember to model.push_to_hub or model.save_pretrained into the same model repository or directory to avoid having to re-export the model every time.

All keyword arguments passed via model_kwargs will be passed on to ORTModel.from_pretrained or OVBaseModel.from_pretrained. The most useful arguments are:

• provider: (Only if backend="onnx") ONNX Runtime provider to use for loading the model, e.g. "CPUExecutionProvider" . See https://onnxruntime.ai/docs/execution-providers/ for possible providers. If not specified, the strongest provider (E.g. "CUDAExecutionProvider") will be used.
• file_name: The name of the ONNX file to load. If not specified, will default to "model.onnx" or otherwise "onnx/model.onnx" for ONNX, and "openvino_model.xml" and "openvino/openvino_model.xml" for OpenVINO. This argument is useful for specifying optimized or quantized models.
• export: A boolean flag specifying whether the model will be exported. If not provided, export will be set to True if the model repository or directory does not already contain an ONNX or OpenVINO model.

For example:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
"all-MiniLM-L6-v2",
backend="onnx",
model_kwargs={
</tr></table>

... (truncated)

Commits

• 402198f Release v3.2.0
• adbf0ba Add backends: ONNX & OpenVINO + ONNX optimization, quantization (#2712)
• 130bdea Add from_model2vec/from_distillation to docs (#2978)
• f85b502 Refine mine_hard_negatives arguments (#2977)
• 7855327 [feat] Add lightning-fast StaticEmbedding module based on model2vec (#2961)
• 07ae865 [ENH] Improve code quality in CrossEncoder.rank + test (#2947)
• b6f1142 Add negatives in CachedGISTEmbedLoss (#2946)
• a7cc68f [model cards] Keep evaluation order in training logs if there's multiple ev...
• 4f43de6 [feat] Add query prompts to Information Retrieval Evaluator (#2951)
• 80393d0 [fix] Only save first module in root if "save_in_root" is specified. (#2957)
• See full diff in compare view

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