Internally, all-MiniLM-L12-v2 is a Transformer-based encoder that converts input text into a dense semantic representation. The model processes text by first tokenizing it into subword units, embedding those tokens, and passing them through 12 Transformer layers. Each layer applies self-attention and feed-forward transformations to capture contextual relationships between words. The result is a sequence of contextualized token embeddings that encode meaning based on surrounding tokens.
To produce a single vector for a sentence or paragraph, the model applies a pooling strategy over the token embeddings. In most sentence embedding setups, this is mean pooling: the vectors for all tokens are averaged to form one fixed-length embedding. This pooled vector is then optionally normalized. The training objective typically uses contrastive learning, where semantically similar sentence pairs are pulled closer together in vector space and dissimilar pairs are pushed apart. Over time, this shapes the embedding space so that distance corresponds to semantic similarity.
In practice, this internal design makes all-MiniLM-L12-v2 fast and predictable. It does not reason or generate text; it only encodes meaning. That simplicity is why it works well as the first stage of retrieval systems. When combined with a vector database such as Milvus or Zilliz Cloud, the internal mechanics of the model align well with approximate nearest neighbor search. The model defines the vector space; the database indexes that space efficiently. Understanding this separation helps developers debug retrieval issues: if results are poor, the problem is often chunking, training domain mismatch, or indexing parameters—not the Transformer math itself.
For more information, click here: https://zilliz.com/ai-models/all-minilm-l12-v2