baa.ai · Merino-Pro

The premium unified retrieval model — bi-encoder embedding and cross-encoder reranking in one package, over a single shared word-embedding table. A 1024-dimensional multilingual model, by BAA AI (Black Sheep AI). ~872M params (fp16, ~1.78 GB).

Get the optimal model for your data

Merino-Pro is baa.ai's flagship default. But the best embedder + reranker is corpus-specific — the ideal choice depends on your documents and your notion of relevance. baa.ai offers exclusive tooling that identifies the optimal embedding and reranking models for your specific data, so you ship the smallest models that maximize document recovery on your corpus. For a tailored recommendation, reach out to baa.ai.

What it is

A two-role retrieval model over a shared input word-embedding matrix (~256M params, stored once). The bi-encoder embedder and cross-encoder reranker are both built on the xlm-roberta-large backbone and co-trained to share a single word-embedding table at no quality loss, while each role keeps its native layers and head. Footprint ~0.77x the two separate models.

• Embed role: bi-encoder, 1024-d, L2-normalized. Prepend "query: " to queries.
• Rerank role: cross-encoder, single relevance logit per (query, document) pair.
• Router: call .embed(...) or .rerank(...).

Usage

from modeling_baa import BaaEmbeddingReranker   # included in this repo

m = BaaEmbeddingReranker("baa-ai/Merino-Pro")
qv = m.embed(["my query"], is_query=True)[0]     # 1024-d normalized
dv = m.embed(["doc a", "doc b"])
ranked = m.rerank("my query", ["doc a", "doc b"], top_k=10)   # [(doc, score), ...]

License & attribution

• BAA Contributions (shared-embedding architecture, router/loader code, packaging, weights, docs) are proprietary to BAA AI (Black Sheep AI) — see LICENSE.
• Incorporates the xlm-roberta-large backbone under the MIT License — see LICENSE-xlm-roberta-large.txt.

© 2026 BAA AI (Black Sheep AI) — baa.ai. Provided "as is" without warranty.