build(deps): bump sentence-transformers from 3.1.1 to 5.1.1

[dependabot]

Bumps sentence-transformers from 3.1.1 to 5.1.1.

Release notes

Sourced from sentence-transformers's releases.

v5.1.1 - Explicit incorrect arguments, fixes for multi-GPU, evaluator, and hard negative

This patch makes Sentence Transformers more explicit with incorrect arguments and introduces some fixes for multi-GPU processing, evaluators, and hard negatives mining.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==5.1.1
Inference only, use one of:
pip install sentence-transformers==5.1.1
pip install sentence-transformers[onnx-gpu]==5.1.1
pip install sentence-transformers[onnx]==5.1.1
pip install sentence-transformers[openvino]==5.1.1

Error if unused kwargs is passed & get_model_kwargs (#3500)

Some SentenceTransformer or SparseEncoder models support custom model-specific keyword arguments, such as jinaai/jina-embeddings-v4. As of this release, calling model.encode with keyword arguments that aren't used by the model will result in an error.

>>> from sentence_transformers import SentenceTransformer
>>> model = SentenceTransformer("all-MiniLM-L6-v2")
>>> model.encode("Who is Amelia Earhart?", normalize=True)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "[sic]/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "[sic]/SentenceTransformer.py", line 983, in encode
    raise ValueError(
ValueError: SentenceTransformer.encode() has been called with additional keyword arguments that this model does not use: ['normalize']. As per SentenceTransformer.get_model_kwargs(), this model does not accept any additional keyword arguments.

Quite useful when you, for example, accidentally forget that the parameter to get normalized embeddings is normalize_embeddings. Prior to this version, this parameter would simply quietly be ignored.

To check which custom extra keyword arguments may be used for your model, you can call the new get_model_kwargs method:

>>> from sentence_transformers import SentenceTransformer, SparseEncoder
>>> SentenceTransformer("all-MiniLM-L6-v2").get_model_kwargs()
[]
>>> SentenceTransformer("jinaai/jina-embeddings-v4", trust_remote_code=True).get_model_kwargs()
['task', 'truncate_dim']
>>> SparseEncoder("opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill").get_model_kwargs()
['task']

Note: You can always pass the task parameter, it's the only model-specific parameter that will be quietly ignored. This means that you can always use model.encode(..., task="query") and model.encode(..., task="document").

Minor Features

... (truncated)

Commits

• 22ff509 Release v5.1.1
• 5ad8a44 Merge branch 'master' into v5.1-release
• 1def8d3 Fix the number of missing negatives in mine_hard_negatives (#3504)
• 2e077fb fix: add makedirs to informationretrievalevaluator (#3516)
• 20c4820 Fix:Import SentenceTransformer class explicitly in losses module (#3521)
• 7240b33 [feat] add get_model_kwargs method; throw error if unused kwarg is passed (...
• 560cc33 always pass input_ids, attention_mask, token_type_ids, inputs_embeds ...
• bd91098 Update rasyosef/splade-mini MSMARCO and BEIR-13 benchmark scores in pretraine...
• ad8d27d Add Support for Knowledgeable Passage Retriever (KPR) (#3495)
• 5b18f36 [feat] Use encode_document and encode_query in mine_hard_negatives (#3502)
• Additional commits viewable in compare view

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[Likhithsai2580]

@dependabot reopen

[dependabot]

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[dependabot]

Superseded by #15.