Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:10053
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use wwydmanski/modernbert-pubmed-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use wwydmanski/modernbert-pubmed-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wwydmanski/modernbert-pubmed-v0.1") sentences = [ "Fluorescence quenching of tryptophan residues", "Fluorescence of buried tyrosine residues in proteins. ", "A fluorescence quenching study of tryptophanyl residues of (Ca2+ + Mg2+)-ATPase from sarcoplasmic reticulum. ", "Some hormonal influences on the acetylation of sulfanilamide in vivo. " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2a58b002adeef5de57d90a361bb11b9622f1865648cf7fd66d049665aff8bcf
- Size of remote file:
- 596 MB
- SHA256:
- 506efe69a60e33b0a0c3723261e8df988812f3d7c476d1074ea78ecf2951fed8
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