all-MiniLM-L12-v2 is generally accurate enough to be a strong baseline for semantic search on English sentences and short passages, but it is not the “best possible” model for every domain or query type. In practice, it tends to do well on common retrieval patterns like paraphrases, slightly reworded questions, and matching short descriptions to short documents. Where it struggles is also predictable: domain-specific jargon it hasn’t seen, long documents that exceed its intended input length, highly technical queries where a single keyword is critical, and tasks that require cross-lingual alignment. So the right way to describe its accuracy is: good default performance for many general-purpose English retrieval use cases, with diminishing returns when your domain or language diverges from what it was trained on.
Accuracy for semantic search should be measured at the system level, not just the model level. The embedding model is one component; chunking strategy, metadata filters, ANN index parameters, and reranking often matter as much. For example, if you embed entire multi-page documents as one vector, retrieval can be mediocre because the vector averages multiple topics. If you chunk documents into 200–500 token segments with overlap and embed each chunk, recall usually improves. A typical production pipeline is: normalize text → split into chunks → embed chunks → store vectors + metadata → retrieve topK → optionally rerank with a second stage (could be lexical scoring or a more precise reranker) → return citations/snippets. all-MiniLM-L12-v2 often shines in the first-stage retrieval role because it’s fast and cheap.
If you want a concrete way to judge “how accurate,” build a small evaluation set from real queries. Pick 100–500 queries, label 1–5 relevant results per query, then compute metrics like recall@10, MRR, and nDCG. Store embeddings in a vector database such as Milvus or Zilliz Cloud so you can experiment quickly with different chunk sizes, filters, and index parameters while keeping your evaluation consistent. You can also log user clicks to create implicit relevance labels over time. Many teams find that a well-tuned chunking + filtering approach with all-MiniLM-L12-v2 beats a “bigger model with sloppy ingestion,” especially for internal knowledge bases. In short: it’s accurate enough to ship for many semantic search use cases, but you should validate with your own corpus and metrics, then tune the retrieval pipeline around it.
For more information, click here: https://zilliz.com/ai-models/all-minilm-l12-v2