How to use s2593817/sft-sql-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("s2593817/sft-sql-embedding") sentences = [ "SELECT DISTINCT count(alias3.col1) , alias1.col2 FROM table1 AS alias1 JOIN table2 AS alias2 ON alias1.col2 = alias2.col2 JOIN table3 AS alias3 ON alias1.col1 = alias3.col1 WHERE alias2.col3 = str AND alias3.year = num GROUP BY alias1.col2", "SELECT col1 , avg(col2) FROM table1 WHERE col3 LIKE str GROUP BY col1", "SELECT col1 , col2 FROM table1 WHERE col3 LIKE str GROUP BY col1 ORDER BY count(*) DESC LIMIT num", "SELECT col1 , avg(col2) FROM table1 GROUP BY col1 ORDER BY avg(col2)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
xccr