baa.ai · Merino-Large-v2

One model that does both halves of RAG retrieval — bi-encoder embedding and cross-encoder reranking — over a single shared word-embedding table. A 1024-dimensional multilingual model, ~872M parameters, by BAA AI (Black Sheep AI).

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What it is

A two-role retrieval model over a shared input word-embedding matrix (stored once). The bi-encoder embedder and a large cross-encoder reranker are built on the same xlm-roberta-large backbone, so their word-embedding table is stored a single time and injected into the reranker at load — a smaller download at no measured quality loss, with no retraining.

• 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-Large-v2")
qv = m.embed(["how does a cross-encoder reranker work?"], is_query=True)[0]
dv = m.embed(["a cross-encoder scores a (query, document) pair jointly",
              "bi-encoders embed query and document separately for fast retrieval"])
ranked = m.rerank("how does a cross-encoder reranker work?",
                  ["a cross-encoder scores a (query, document) pair jointly",
                   "the mitochondria is the powerhouse of the cell"])
# -> [(doc, score), ...] sorted best-first

Specs

Embedding dim	1024
Parameters	~872M (embedder + reranker, shared word-embedding table)
Languages	multilingual
Max sequence length	512
Hardware	CPU / edge / GPU

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.