sentence-transformers How to use spl4shedEdu/gte_ISM with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("spl4shedEdu/gte_ISM", trust_remote_code=True)
sentences = [
"pendaflex surehook extra capacity reinforced hanging folders letter size assorted colors total of 20 folders per box 6152x2 asstus files rasps hand tools depotus pendaflex surehook extra capacity reinforced hanging folders feature longer plastic hooks with builtin tension springs that help keep files on track the pressboardreinforced insert allows them to expand 2 for up to 400 sheets durable 11 pt covers wont buckle under pressure includes an 85 x 11 printerready label sheet and clear tabs for placement in 5 positions paper made from 10 recycled fiber with 10 postconsumer fiber letter size assorted colors blue yellow red bright green orange 20 per boxus pendaflexus 6152x2asst officeproducts",
"pendaflex surehook extra capacity reinforced hanging folders letter size assorted colors total of 20 folders per box 6152x2 asstus files rasps hand tools depotus pendaflex surehook extra capacity reinforced hanging folders feature longer plastic hooks with builtin tension springs that help keep files on track the pressboardreinforced insert allows them to expand 2 for up to 400 sheets durable 11 pt covers wont buckle under pressure includes an 85 x 11 printerready label sheet and clear tabs for placement in 5 positions paper made from 10 recycled fiber with 10 postconsumer fiber letter size assorted colors blue yellow red bright green orange 20 per boxus pendaflexus 6152x2asst officeproducts",
"nissan cwtwb1u789 factory oem key fob keyless entry remote alarm replace null nissan 731015045730 798426001120 automotive",
"mf digital scribe cddvdbd print station picojet2 300disc capacity station picojet2 capacity cd solutions mf digital scribe cddvdbd print station picojet2 300disc capacity mf digital 739410810199 739410811042 739410531797 computersandaccessories"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
