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]==3.4.1Full 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]])Previously, loading a Model2Vec model required you to load a `StaticEmbedding` module.
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding
# Download from the 🤗 Hub
module = StaticEmbedding.from_model2vec("minishlab/potion-base-8M")
model = SentenceTransformer(modules=[module], 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]])Model2Vec was the inspiration of the recent Static Embedding work; all of these models can be used to approach the performance of normal transformer-based embedding models at a fraction of the latency. For example, both Model2Vec and Static Embedding models are ~25x faster than tiny embedding models on a GPU and ~400x faster than those models on a CPU.
Bug Fix
- Using
local_files_only=Truestill triggered a request to Hugging Face for the model card metadata; this has been resolved in (#3202).
All Changes
- fix loss name in documentation of CachedMultipleNegativesRankingLoss by @JINO-ROHIT in #3191
- Bump jinja2 from 3.1.4 to 3.1.5 in /docs by @dependabot in #3192
- minor typo in MegaBatchMarginLoss by @JINO-ROHIT in #3193
- Fix type hint of
StaticEmbedding.__init__by @altescy in #3196 - [
integration] Work towards full model2vec integration by @tomaarsen in #3182 - Don't call
set_base_modelwhenlocal_files_only=Trueby @Davidyz in #3202
New Contributors
- @dependabot made their first contribution in #3192
- @altescy made their first contribution in #3196
- @Davidyz made their first contribution in #3202
Full Changelog: v3.4.0...v3.4.1