sentence-transformers How to use elnurgar/qwen3-embedding-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("elnurgar/qwen3-embedding-finetuned")
sentences = [
"What was the pre-tax net favorable prior period development for 2022 and what factors contributed to it?",
"In 2023, $2.2 billion or 5% was primarily related to patient co-pay assistance, cash discounts for prompt payment, distributor fees, and sales return provisions.",
"The pre-tax net favorable prior period development for 2022 was $876 million. Adverse development factors like molestation claims, primarily reviver statute-related compromising $155 million, and $113 million related to legacy asbestos and environmental exposures significantly influenced this outcome.",
"The Company currently holds a broad collection of intellectual property rights relating to certain aspects of its hardware devices, accessories, software and services. This includes patents, designs, copyrights, trademarks and other forms of intellectual property rights in the U.S. and various foreign countries. Although the Company believes the ownership of such intellectual property rights is an important factor in differentiating its business and that its success does depend in part on such ownership, the Company relies primarily on the innovative skills, technical competence and marketing abilities of its personnel."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
