bge-small-en-v1.5 and bge-base-en-v1.5 are both feature-extraction models. See each entry for specifics.
Small English dense embedding model from BAAI's BGE (BAAI General Embedding) series, producing 384-dimensional vectors via MIT license. Optimized for MTEB retrieval benchmarks through a retrieval-focused training strategy, it achieves competitive scores relative to its parameter count. Suited for embedding workflows where throughput and cost matter more than peak accuracy.
BGE-Base-EN-v1.5 is BAAI's mid-tier English embedding model in the v1.5 series, producing 768-dimensional vectors. It balances accuracy and compute cost between the small (384d) and large (1024d) variants, making it a practical default for English retrieval tasks where storage and inference overhead of the large model are undesirable. MIT licensed with ONNX export.
Pick based on your compute budget and specific task requirements.