sentence-transformers How to use bwang0911/bge-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("bwang0911/bge-int8")
sentences = [
"Which university did Cheryl Miller attend?",
"Cheryl Miller male or female, to be named an All-American by \"Parade\" magazine four times. Averaging 32.8 points and 15.0 rebounds a game, Miller was Street & Smith's national High School Player of the Year in both 1981 and 1982. In her senior year she scored 105 points in a game against Norte Vista High School. She set California state records for points scored in a single season (1156), and points scored in a high school career (3405). At the University of Southern California (USC), the 6 ft. 2 in. (1.87 m) Miller played the forward position. She was a four-year letter",
"1979 Formula One season 1979 Formula One season The 1979 Formula One season was the 33rd season of FIA Formula One motor racing. It featured the 1979 World Championship of F1 Drivers and the 1979 International Cup for F1 Constructors which were contested concurrently over a fifteen-round series which commenced on 21 January 1979, and ended on 7 October. The season also included three non-championship Formula One races. Jody Scheckter of Scuderia Ferrari won the 1979 World Championship of F1 Drivers while Scuderia Ferrari won 1979 International Cup for F1 Constructors. Gilles Villeneuve made it a 1–2 for Ferrari in the championship, concluding a",
"Cheryl Miller April 30, 2014, she was named the women's basketball coach at Langston University by athletic director Mike Garrett. On May 26, 2016, she was named the women's basketball coach at California State Los Angeles by athletic director Mike Garrett. Cheryl Miller serves as a sideline reporter for the \"NBA on TNT\"’s Thursday night doubleheader coverage for TNT Sports. She also made appearances on NBA TV during the 2008-09 NBA season as a reporter and analyst. Miller joined Turner Sports in September 1995 as an analyst and reporter for the \"NBA on TBS\" and TNT. She did make occasional appearances as"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
