chore(deps): Bump sentence-transformers from 3.2.0 to 3.3.0 in /backend/python/sentencetransformers

[dependabot]

Bumps sentence-transformers from 3.2.0 to 3.3.0.

Release notes

Sourced from sentence-transformers's releases.

v3.2.1 - Patch CLIP loading, small ONNX fix, compatibility with other libraries

This patch release fixes some small bugs, such as related to loading CLIP models, automatic model card generation issues, and ensuring compatibility with third party libraries.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==3.2.1
Inference only, use one of:
pip install sentence-transformers==3.2.1
pip install sentence-transformers[onnx-gpu]==3.2.1
pip install sentence-transformers[onnx]==3.2.1
pip install sentence-transformers[openvino]==3.2.1

Fixing Loading non-Transformer models

In v3.2.0, a non-Transformer based model (e.g. CLIP) would not load correctly if the model was saved in the root of the model repository/directory. This has been resolved in #3007.

Throw error if StaticEmbedding-based model is finetuned with incompatible losses

The following losses are not compatible with StaticEmbedding-based models:

• CachedGISTEmbedLoss
• CachedMultipleNegativesRankingLoss
• CachedMultipleNegativesSymmetricRankingLoss
• DenoisingAutoEncoderLoss
• GISTEmbedLoss

An error is now thrown when one of these are used with a StaticEmbedding-based model. I recommend using MultipleNegativesRankingLoss to finetune these models, e.g. as in https://huggingface.co/tomaarsen/static-bert-uncased-gooaq. Note: to get good performance, you must use much higher learning rates than otherwise. In my experiments, 2e-1 worked well.

Patch ONNX model when the model uses output_hidden_states

For example, this script used to fail, but passes now:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer(
"distiluse-base-multilingual-cased",
backend="onnx",
model_kwargs={"provider": "CPUExecutionProvider"},
)
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings.shape)

All changes

• Bump optimum version by @​echarlaix in UKPLab/sentence-transformers#2984
• [docs] Update the training snippets for some losses that should use the v3 Trainer by @​tomaarsen in UKPLab/sentence-transformers#2987
• [enh] Throw error if StaticEmbedding-based model is trained with incompatible loss by @​tomaarsen in UKPLab/sentence-transformers#2990

... (truncated)

Commits

• ea49e01 Release v3.3.0
• 6347b75 Moved Model Card Callback init in Trainer to a separate function (#3047)
• 7ede83b [enh] Add Support for multiple adapters on Transformers-based models (#3046)
• 6baee57 [fix] Fix model loading inconsistency after Peft training by using PeftModel ...
• 7be3eac [feat] Trainer with prompts and prompt masking (#2964)
• 15d3898 [docs] Add some missing docs for include_prompt in Pooling (#3042)
• 5bccba3 [chore] If Transformers 4.46.0, use processing_class instead of tokenizer w...
• cb81136 [fix] Fix loading pre-exported OV/ONNX model if export=False (#3036)
• 7d99ca9 [tests] Remove evaluation_steps from model.fit test without evaluator (#3037)
• 1cb196a [deprecate] Drop Python 3.8 support due to EOL (#3033)
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