How to use qcsun/financial-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("qcsun/financial-embedding") sentences = [ "PACER FDS TR SWAN SOS FLEX JULY ETF(PSFJ)周线级别突破关键阻力位，技术面呈现强势", "市场解读行业政策对NUVL的积极影响", "PACER FDS TR SWAN SOS FLEX JULY ETF(PSFJ)技术指标发出看涨信号，短期或延续涨势", "FTXR" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
