[dependabot]

Bumps sentence-transformers from 3.1.1 to 3.4.1.

Release notes

Sourced from sentence-transformers's releases.

v3.4.1 - Model2Vec compatibility & offline model fix

This release introduces a convenient compatibility with Model2Vec models, and fixes a bug that caused an outgoing request even when using a local model.

Install this version with

# Training + Inference
pip install sentence-transformers[train]==3.4.1
Inference only, use one of:
pip install sentence-transformers==3.4.1
pip install sentence-transformers[onnx-gpu]==3.4.1
pip install sentence-transformers[onnx]==3.4.1
pip install sentence-transformers[openvino]==3.4.1

Full Model2Vec integration

This release introduces support to load an efficient Model2Vec embedding model directly in Sentence Transformers:

from sentence_transformers import SentenceTransformer
Download from the 🤗 Hub
model = SentenceTransformer(
"minishlab/potion-base-8M",
device="cpu",
)
Run inference
sentences = [
'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?',
'To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms.',
'In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
[3, 1024]
Get the similarity scores for the embeddings
similarities = model.similarity(embeddings[0], embeddings[1:])
print(similarities)
tensor([[0.8085, 0.4884]])

from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding
Download from the 🤗 Hub
module = StaticEmbedding.from_model2vec("minishlab/potion-base-8M")
</tr></table>

... (truncated)

Commits

• 7d52a06 Release v3.4.1
• 309cc66 Merge branch 'master' into v3.4-release
• e2a0098 Don't call set_base_model when local_files_only=True (#3202)
• 3fbc183 [integration] Work towards full model2vec integration (#3182)
• db6ce94 Fix type hint of StaticEmbedding.init (#3196)
• dfef1d6 minor typo in MegaBatchMarginLoss (#3193)
• b48abfa Bump jinja2 from 3.1.4 to 3.1.5 in /docs (#3192)
• c9d3c96 fix loss name in documentation of CachedMultipleNegativesRankingLoss (#3191)
• b765d85 Increment dev version
• 90be5b7 Merge branch 'master' of https://github.com/UKPLab/sentence-transformers into...
• Additional commits viewable in compare view

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