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- from sentence_transformers import SentenceTransformer
- import time
- t0 = time.time()
- model = SentenceTransformer("BAAI/bge-large-zh-v1.5", max_seq_length=512)
- vec_dim = model.get_sentence_embedding_dimension()
- t1 = time.time()
- print(f"Model loaded in {t1-t0:.1f}s, vec_dim={vec_dim}")
- vec = model.encode(["肠道菌群和抑郁有什么关系？"])
- print(f"Sample vec shape: {vec.shape}")
- print(f"Sample vec[0][:5]: {vec[0][:5]}")
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