sentence-transformers How to use mariakrissmer/alias_demo_model with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("mariakrissmer/alias_demo_model")
sentences = [
"The expression of FTL, FTH1, TMSB4X, B2M, VIM, C7orf26, ACTB, PFN1, LYZ, S100A4, CD74, TMSB10, FAU, CST3, HLA-E, EEF1A1, SAT1, TYROBP, LGALS1, HLA-DPB1, S100A9, DUSP1, RBM3, MALAT1, OAZ1, S100A6, HLA-DPA1, COTL1, PTMA, GNB2L1, YBX1, HLA-DRA, ATP5J2, PSAP, CYBA, SH3BGRL3, LST1, EIF1, H3F3B, UBA52, HLA-DRB1, IFITM3, GSTP1, SERF2, EMP3, FCER1G, PABPC1, ARPC2, GAPDH, TPT1 aligns with a CD14+ Monocytes identity.",
"This cell expresses the genes: FTH1, FTL, S100A9, C16orf13, ACTB, TMSB4X, LYZ, B2M, S100A8, S100A6, MALAT1, PLA2G7, CST3, SAP18, CTSS, S100A4, SAT1, FOS, TMSB10, TYROBP, EIF5, GPX1, HCST, LGALS1, EIF1, DUSP1, PSMB9, G0S2, ANXA1, GAPDH, PFDN5, CYBA, H3F3B, OAZ1, JUNB, ZFP36, LGALS2, EEF1D, SF1, NACA, NFKBIA, LGALS3, ACTG1, CD37, SH3BGRL3, IFI6, S100A11, CD74, HLA-C, HLA-B.",
"Cells expressing MALAT1, B2M, PAXIP1-AS1, TMSB4X, EEF1A1, TMSB10, JUNB, UBA52, PTMA, TPT1, FTH1, NACA, EIF1, EEF1B2, BTF3, EEF1D, BTG1, HLA-C, FOS, DDX5, H3F3B, KLF2, NPM1, DUSP1, GNB2L1, HSPA8, HNRNPA1, GLTSCR2, JUN, LTB, TOMM7, FAU, CFL1, COX4I1, MYL12A, IL7R, HNRNPA0, ACTB, PABPC1, FTL, CD48, TOMM20, SRSF7, DDX18, HLA-A, HLA-B, NDUFB11, NONO, SH3BGRL, EIF3H often belong to the CD4 T cells lineage.",
"The expression pattern of FTL, TMSB4X, S100A9, S100A8, FTH1, B2M, LYZ, MALAT1, ACTB, S100A6, GPX1, DHRS4L2, S100A10, S100A4, SAT1, TMSB10, EEF1A1, EIF1, H3F3B, LGALS1, CYBA, OAZ1, TYROBP, GNB2L1, FAU, ATP5G2, MYL6, NACA, NF1, PTMA, HLA-A, VIM, SRGN, NEAT1, BTG1, TPT1, CST3, SH3BGRL3, FCN1, KLF6, CTSD, ARPC3, CFD, UBA52, CTSS, CAPG, FYB, BTF3, HLA-C, AIF1 strongly indicates a CD14+ Monocytes cell."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
