from sentence_transformers import SentenceTransformer

model = SentenceTransformer("trantuan1701/product-granite-97m-vietnamese")

query_embeddings = model.encode(
    ["đèn led downlight at02 đổi màu"],
    normalize_embeddings=True,
)

document_embeddings = model.encode(
    ["product document text..."],
    normalize_embeddings=True,
)

Important: this Granite model was trained without query: or passage: prefixes.

Training summary

Loss: MultipleNegativesRankingLoss
Max sequence length: 512
Dataset: 15,000 query-document pairs, 1,000 product documents
Validation split: deterministic by doc_id

Limitations

Current validation loss is a training diagnostic only. A full retrieval benchmark should report Recall@K, MRR, and NDCG before production selection.