sentence-transformers How to use potsu-potsu/snowflake-embed-mrl-train40k with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("potsu-potsu/snowflake-embed-mrl-train40k")
sentences = [
"Represent this sentence for searching relevant passages: List the deadliest viruses in the world.",
"Mediator is a large multiprotein complex conserved in all eukaryotes, which has \na crucial coregulator function in transcription by RNA polymerase II (Pol II). \nHowever, the molecular mechanisms of its action in vivo remain to be understood. \nMed17 is an essential and central component of the Mediator head module. In this \nwork, we utilised our large collection of conditional temperature-sensitive \nmed17 mutants to investigate Mediator's role in coordinating preinitiation \ncomplex (PIC) formation in vivo at the genome level after a transfer to a \nnon-permissive temperature for 45 minutes. The effect of a yeast mutation \nproposed to be equivalent to the human Med17-L371P responsible for infantile \ncerebral atrophy was also analyzed. The ChIP-seq results demonstrate that med17 \nmutations differentially affected the global presence of several PIC components \nincluding Mediator, TBP, TFIIH modules and Pol II. Our data show that Mediator \nstabilizes TFIIK kinase and TFIIH core modules independently, suggesting that \nthe recruitment or the stability of TFIIH modules is regulated independently on \nyeast genome. We demonstrate that Mediator selectively contributes to TBP \nrecruitment or stabilization to chromatin. This study provides an extensive \ngenome-wide view of Mediator's role in PIC formation, suggesting that Mediator \ncoordinates multiple steps of a PIC assembly pathway.",
"mTOR complex 2 (mTORC2) signaling is upregulated in multiple types of human \ncancer, but the molecular mechanisms underlying its activation and regulation \nremain elusive. Here, we show that microRNA-mediated upregulation of Rictor, an \nmTORC2-specific component, contributes to tumor progression. Rictor is \nupregulated via the repression of the miR-424/503 cluster in human prostate and \ncolon cancer cell lines that harbor c-Src upregulation and in Src-transformed \ncells. The tumorigenicity and invasive activity of these cells were suppressed \nby re-expression of miR-424/503. Rictor upregulation promotes formation of \nmTORC2 and induces activation of mTORC2, resulting in promotion of tumor growth \nand invasion. Furthermore, downregulation of miR-424/503 is associated with \nRictor upregulation in colon cancer tissues. These findings suggest that the \nmiR-424/503-Rictor pathway plays a crucial role in tumor progression.",
"This year marks the 100th anniversary of the deadliest event in human history. \nIn 1918-1919, pandemic influenza appeared nearly simultaneously around the globe \nand caused extraordinary mortality (an estimated 50-100 million deaths) \nassociated with unexpected clinical and epidemiological features. The \ndescendants of the 1918 virus remain today; as endemic influenza viruses, they \ncause significant mortality each year. Although the ability to predict influenza \npandemics remains no better than it was a century ago, numerous scientific \nadvances provide an important head start in limiting severe disease and death \nfrom both current and future influenza viruses: identification and substantial \ncharacterization of the natural history and pathogenesis of the 1918 causative \nvirus itself, as well as hundreds of its viral descendants; development of \nmoderately effective vaccines; improved diagnosis and treatment of \ninfluenza-associated pneumonia; and effective prevention and control measures. \nRemaining challenges include development of vaccines eliciting significantly \nbroader protection (against antigenically different influenza viruses) that can \nprevent or significantly downregulate viral replication; more complete \ncharacterization of natural history and pathogenesis emphasizing the protective \nrole of mucosal immunity; and biomarkers of impending influenza-associated \npneumonia."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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