sentence-transformers How to use tomaarsen/inference-free-splade-bert-tiny-nq-3e-3-lambda-corpus with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("tomaarsen/inference-free-splade-bert-tiny-nq-3e-3-lambda-corpus")
sentences = [
"where is the tiber river located in italy",
"Sales taxes in British Columbia On 1 July 2010, the PST and GST were combined into the Harmonized Sales Tax (HST) levied according to the provisions of the GST. The conversion to HST was controversial. Popular opposition led to a referendum on the tax system, the first such referendum in the Commonwealth of Nations, resulting in the province reverting to the former PST/GST model on 1 April 2013.",
"Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.",
"Water in California California's limited water supply comes from two main sources: surface water, or water that travels or gathers on the ground, like rivers, streams, and lakes; and groundwater, which is water that is pumped out from the ground. California has also begun producing a small amount of desalinated water, water that was once sea water, but has been purified."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
