static-retrieval-mrl-en

This is a sentence-transformers model trained on the gooaq, msmarco, squad, s2orc, allnli, paq, trivia_qa, msmarco_10m, swim_ir, pubmedqa, miracl, mldr and mr_tydi datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and is designed to be used for semantic search.

Read our Static Embeddings blogpost to learn more about this model and how it was trained.

• 0 Active Parameters: This model does not use any active parameters, instead consisting exclusively of averaging pre-computed token embeddings.
• 100x to 400x faster: On CPU, this model is 100x to 400x faster than common options like all-mpnet-base-v2. On GPU, it's 10x to 25x faster.
• Matryoshka: This model was trained with a Matryoshka loss, allowing you to truncate the embeddings for faster retrieval at minimal performance costs.
• Evaluations: See Evaluations for details on performance on NanoBEIR, embedding speed, and Matryoshka dimensionality truncation. In short, this model is 87.4% as performant as the commonly used all-mpnet-base-v2.
• Training Script: See train.py for the training script used to train this model from scratch.

See static-similarity-mrl-multilingual-v1 for a general-purpose multilingual static embedding model. It's been trained for semantic textual similarity, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer

• Maximum Sequence Length: inf tokens

• Output Dimensionality: 1024 tokens

• Similarity Function: Cosine Similarity

• Training Datasets:

  • gooaq
  • msmarco
  • squad
  • s2orc
  • allnli
  • paq
  • trivia_qa
  • msmarco_10m
  • swim_ir
  • pubmedqa
  • miracl
  • mldr
  • mr_tydi

• Language: en

• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): StaticEmbedding(
    (embedding): EmbeddingBag(30522, 1024, mode='mean')
  )
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/static-retrieval-mrl-en-v1")
# 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(e