Rebrand to baa.ai Merino-Nano (backbone-only attribution)

LICENSE

@@ -1,202 +1,24 @@
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+Merino-Nano — Proprietary License
+Copyright (c) 2026 BAA AI (Black Sheep AI). All rights reserved.
+
+1. SCOPE. This license governs the "BAA Contributions" in this package: the
+   shared word-embedding architecture and configuration, the router / loader
+   code (modeling_baa.py), the model packaging, BAA AI's weight contributions,
+   the model card, and associated documentation.
+
+2. GRANT. No right to use, reproduce, modify, distribute, sublicense, or create
+   derivative works of the BAA Contributions is granted except under a separate
+   written agreement with BAA AI (Black Sheep AI).
+
+3. THIRD-PARTY COMPONENT. This package incorporates the MiniLM-L6-H384-uncased backbone,
+   provided under the MIT License — see LICENSE-minilm.txt. The MIT terms govern that
+   backbone component only; nothing in this license limits any rights you have
+   under the MIT License with respect to it.
+
+4. NO WARRANTY. THE PACKAGE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+   EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO MERCHANTABILITY, FITNESS FOR
+   A PARTICULAR PURPOSE, AND NONINFRINGEMENT. IN NO EVENT SHALL BAA AI BE LIABLE
+   FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY ARISING FROM OR IN CONNECTION WITH
+   THE PACKAGE OR ITS USE.
+
+Contact: BAA AI (Black Sheep AI) — baa.ai

LICENSE-minilm.txt

@@ -0,0 +1,23 @@
+Backbone component: MiniLM-L6-H384-uncased — MIT License
+
+MIT License
+
+Copyright (c) Microsoft Corporation.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+copies or substantial portions of the Software.
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MODEL_CARD.md

@@ -0,0 +1,61 @@
+---
+license: other
+license_name: baa-proprietary
+library_name: sentence-transformers
+tags:
+- retrieval
+- embeddings
+- reranker
+- cross-encoder
+- rag
+- sentence-similarity
+pipeline_tag: sentence-similarity
+---
+
+# baa.ai · Merino-Nano
+
+**One model that does both halves of RAG retrieval — bi-encoder embedding *and* cross-encoder reranking — over a single shared word-embedding table.** A 384-dimensional English model, ~34M parameters, by BAA AI (Black Sheep AI).
+
+## Get the optimal model for *your* data
+
+Merino-Nano is a strong, cost-efficient **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** (stored once). The bi-encoder embedder and a compact cross-encoder reranker are built on the same `MiniLM-L6-H384-uncased` 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, 384-d, L2-normalized.
+- **Rerank role:** cross-encoder, single relevance logit per (query, document) pair.
+- **Router:** call `.embed(...)` or `.rerank(...)`.
+
+## Usage
+
+```python
+from modeling_baa import BaaEmbeddingReranker   # included in this repo
+
+m = BaaEmbeddingReranker("baa-ai/Merino-Nano")
+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 | 384 |
+| Parameters | ~34M (embedder + reranker, shared word-embedding table) |
+| Languages | English |
+| 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 `MiniLM-L6-H384-uncased` backbone under the **MIT License** — see `LICENSE-minilm.txt`.
+
+© 2026 BAA AI (Black Sheep AI) — baa.ai. Provided "as is" without warranty.

NOTICE

@@ -1,30 +1,5 @@
-baa.ai · Embedding-Reranker-Mini-v1
-Copyright (c) 2026 baa.ai
+Merino-Nano
+Copyright (c) 2026 BAA AI (Black Sheep AI). All rights reserved.
 
-This product is a derivative work licensed under the Apache License, Version 2.0.
-It re-packages and modifies the following upstream models, each originally
-licensed under the Apache License, Version 2.0:
-
-  1. sentence-transformers/all-MiniLM-L6-v2
-       Used as the bi-encoder embedder. Provides the canonical (shared)
-       word-embedding table for the combined model.
-       https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
-
-  2. cross-encoder/ms-marco-MiniLM-L-6-v2
-       Used as the cross-encoder reranker. Its word-embedding table has been
-       removed on disk and is injected at load time from the shared table
-       above, reducing the combined footprint by ~26%.
-       https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2
-
-Both upstream models derive from the Microsoft MiniLM architecture
-(microsoft/MiniLM-L6-H384-uncased).
-
-Modifications by baa.ai:
-  - Unified the two models into a single artifact over one shared
-    word-embedding table (the reranker's word-embedding matrix is stored
-    once, in the embedder, and injected at load).
-  - Added a combined loader (modeling_baa_mini.py) exposing embed() and
-    rerank() over the shared backbone.
-
-This NOTICE file is provided in accordance with Section 4(d) of the
-Apache License, Version 2.0. See the LICENSE file for the full license text.
+BAA Contributions: proprietary — see LICENSE.
+Backbone: MiniLM-L6-H384-uncased — MIT License — see LICENSE-minilm.txt.

README.md

@@ -1,5 +1,6 @@
 ---
-license: apache-2.0
+license: other
+license_name: baa-proprietary
 library_name: sentence-transformers
 tags:
 - retrieval
@@ -11,61 +12,50 @@ tags:
 pipeline_tag: sentence-similarity
 ---
 
-# baa.ai · Embedding-Reranker-Mini-v1
+# baa.ai · Merino-Nano
 
-**A single compact model that does both halves of RAG retrieval — bi-encoder embedding *and* cross-encoder reranking — at ~34M parameters.**
+**One model that does both halves of RAG retrieval — bi-encoder embedding *and* cross-encoder reranking — over a single shared word-embedding table.** A 384-dimensional English model, ~34M parameters, by BAA AI (Black Sheep AI).
 
-The embedder and the reranker share one word-embedding table (stored once), so the packaged model is **~26% smaller on disk than shipping the two components separately**, with **no measured loss in retrieval quality**. It runs comfortably on CPU and at the edge.
+## Get the optimal model for *your* data
 
-## Why this model
+Merino-Nano is a strong, cost-efficient **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**.
 
-Most RAG stacks bolt a large embedder onto a large reranker and pay for both. In practice a small, well-matched embedder + reranker recovers the right documents just as well on most corpora — and costs a fraction to serve. This model packages that compact default into a single artifact.
+## What it is
 
-- **Tiny & fast** — ~34M params total; CPU/edge-friendly; low latency.
-- **Two jobs, one download** — embed for retrieval, then rerank the candidates.
-- **Strong default** — a sensible, cost-efficient starting point for production RAG.
+A two-role retrieval model over a **shared input word-embedding matrix** (stored once). The bi-encoder embedder and a compact cross-encoder reranker are built on the same `MiniLM-L6-H384-uncased` 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, 384-d, L2-normalized.
+- **Rerank role:** cross-encoder, single relevance logit per (query, document) pair.
+- **Router:** call `.embed(...)` or `.rerank(...)`.
 
 ## Usage
 
 ```python
-from modeling_baa_mini import BaaMiniEmbeddingReranker   # included in this repo
-
-m = BaaMiniEmbeddingReranker("baa-ai/Embedding-Reranker-Mini-v1")
+from modeling_baa import BaaEmbeddingReranker   # included in this repo
 
-# 1) retrieve: embed query + documents (vectors are L2-normalized; cosine = dot product)
+m = BaaEmbeddingReranker("baa-ai/Merino-Nano")
 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"])
-
-# 2) rerank: score (query, doc) pairs and order them
 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
 ```
 
-Typical pipeline: embed the corpus once, retrieve the top-k by cosine for a query, then `rerank` those k and keep the top few for your LLM.
-
-## Get the optimal models for *your* data
-
-This model is a great **default**. But the best embedder and reranker are **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. If you want that tailored recommendation for your use case, **reach out to baa.ai**.
-
 ## Specs
 
 | | |
 |---|---|
-| Total parameters | ~34M (embedder + reranker, shared word-embedding table) |
 | Embedding dim | 384 |
+| Parameters | ~34M (embedder + reranker, shared word-embedding table) |
+| Languages | English |
 | Max sequence length | 512 |
-| Footprint vs separate models | ~26% smaller on disk, no measured quality loss |
 | Hardware | CPU / edge / GPU |
 
 ## License & attribution
 
-Released under the **Apache License 2.0**. This is a derivative work that re-packages and modifies two Apache-2.0 models into a single shared-backbone artifact (see `NOTICE`):
-- `sentence-transformers/all-MiniLM-L6-v2` (embedder)
-- `cross-encoder/ms-marco-MiniLM-L-6-v2` (reranker)
+- **BAA Contributions** (shared-embedding architecture, router/loader code, packaging, weights, docs) are **proprietary to BAA AI (Black Sheep AI)** — see `LICENSE`.
+- Incorporates the `MiniLM-L6-H384-uncased` backbone under the **MIT License** — see `LICENSE-minilm.txt`.
 
-© baa.ai. Provided "as is" without warranty; see `LICENSE`.
+© 2026 BAA AI (Black Sheep AI) — baa.ai. Provided "as is" without warranty.

config.json

@@ -1,10 +1,15 @@
 {
-  "model_type": "baa-mini-embed-rerank",
-  "description": "Unified MiniLM bi-encoder embedder + cross-encoder reranker sharing one word-embedding table.",
-  "components": {"embedder": "embedder/", "reranker": "reranker/"},
-  "loader": "modeling_baa_mini.BaaMiniEmbeddingReranker",
+  "model_type": "baa-embedding-reranker",
+  "name": "Merino-Nano",
+  "version": "1",
+  "license": "Proprietary \u2014 BAA AI (Black Sheep AI); MiniLM-L6-H384-uncased backbone under MIT",
+  "architecture": "shared-word-embedding: one MiniLM-L6-H384-uncased word-embedding table shared across the embedder and reranker stacks",
   "embedding_dim": 384,
+  "embed_query_prompt": "",
+  "embed_doc_prompt": "",
   "max_seq_length": 512,
-  "total_params_millions": 33.7,
-  "license": "apache-2.0"
-}
+  "params_millions": 34,
+  "backbone": "MiniLM-L6-H384-uncased (MIT)",
+  "trust_remote_code": false,
+  "loader": "modeling_baa.BaaEmbeddingReranker"
+}

embedder/README.md

@@ -1,175 +0,0 @@
----
-base_model:
-  - nreimers/MiniLM-L6-H384-uncased
-language: en
-license: apache-2.0
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
-datasets:
-- s2orc
-- flax-sentence-embeddings/stackexchange_xml
-- ms_marco
-- gooaq
-- yahoo_answers_topics
-- code_search_net
-- search_qa
-- eli5
-- snli
-- multi_nli
-- wikihow
-- natural_questions
-- trivia_qa
-- embedding-data/sentence-compression
-- embedding-data/flickr30k-captions
-- embedding-data/altlex
-- embedding-data/simple-wiki
-- embedding-data/QQP
-- embedding-data/SPECTER
-- embedding-data/PAQ_pairs
-- embedding-data/WikiAnswers
-pipeline_tag: sentence-similarity
----
-
-
-# all-MiniLM-L6-v2
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-## Usage (Sentence-Transformers)
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-import torch.nn.functional as F
-
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-# Normalize embeddings
-sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
-------
-
-## Background
-
-The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
-contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 
-1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
-
-We developed this model during the 
-[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
-organized by Hugging Face. We developed this model as part of the project:
-[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
-
-## Intended uses
-
-Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures 
-the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
-
-By default, input text longer than 256 word pieces is truncated.
-
-
-## Training procedure
-
-### Pre-training 
-
-We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
-
-### Fine-tuning 
-
-We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
-We then apply the cross entropy loss by comparing with true pairs.
-
-#### Hyper parameters
-
-We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
-We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
-a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
-
-#### Training data
-
-We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
-We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
-
-
-| Dataset                                                  | Paper                                    | Number of training tuples  |
-|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
-| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
-| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
-| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
-| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
-| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
-| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |
-| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
-| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
-| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
-| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
-| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
-| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
-| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
-| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
-| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
-| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
-| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
-| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | 
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
-| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
-| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
-| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
-| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
-| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
-| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
-| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
-| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
-| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
-| **Total** | | **1,170,060,424** |

modeling_baa_mini.py → modeling_baa.py

@@ -1,40 +1,64 @@
-"""baa-ai/Embedding-Reranker-Mini-v1 — a unified MiniLM embedder + cross-encoder reranker that share ONE
-word-embedding table. The reranker's word-embedding matrix is stored only once (in the embedder) and injected at
-load, so the combined model is ~26% smaller than shipping the two models separately, at no measured quality cost.
+"""baa.ai unified Embedding+Reranker loader (generic).
+
+A single artifact that does both halves of RAG retrieval — bi-encoder embedding AND cross-encoder reranking —
+over ONE shared word-embedding table. The reranker's word-embedding matrix is stored only once (in the
+embedder) and injected at load, so the packaged model is smaller than shipping the two components separately,
+at no measured quality cost.
+
+Works for BERT-based and XLM-RoBERTa-based stacks alike: the reranker's encoder submodule is resolved
+generically via `reranker.base_model` (so `.bert` / `.roberta` are both handled). Optional per-model query/doc
+prompts are read from config.json (e.g. some models use a "query: " prefix).
 
 Usage:
-    from modeling_baa_mini import BaaMiniEmbeddingReranker
-    m = BaaMiniEmbeddingReranker("path/to/baa-ai-Embedding-Reranker-Mini-v1")
-    qv = m.embed(["what is a cross-encoder?"], is_query=True)      # bi-encoder retrieval vectors (normalized)
-    dv = m.embed(["a cross-encoder scores a (query, doc) pair jointly", "the mitochondria is the powerhouse"])
+    from modeling_baa import BaaEmbeddingReranker
+    m = BaaEmbeddingReranker("path/to/model-dir")
+    qv = m.embed(["what is a cross-encoder?"], is_query=True)         # normalized bi-encoder vectors
+    dv = m.embed(["a cross-encoder scores a (query, doc) pair jointly"])
     ranked = m.rerank("what is a cross-encoder?", ["doc A ...", "doc B ..."])   # [(doc, score), ...] desc
 """
-import os, torch
+import os, json, torch
 from safetensors.torch import load_file
 from sentence_transformers import SentenceTransformer
 from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
 
 
-class BaaMiniEmbeddingReranker:
+class BaaEmbeddingReranker:
     def __init__(self, path=None, device=None):
         path = path or os.path.dirname(os.path.abspath(__file__))
         self.device = device or ("mps" if torch.backends.mps.is_available()
                                  else ("cuda" if torch.cuda.is_available() else "cpu"))
+        cfg = {}
+        cfg_path = os.path.join(path, "config.json")
+        if os.path.exists(cfg_path):
+            cfg = json.load(open(cfg_path))
+        self.q_prompt = cfg.get("embed_query_prompt", "") or ""
+        self.d_prompt = cfg.get("embed_doc_prompt", "") or ""
+        trc = bool(cfg.get("trust_remote_code", False))
         emb_dir, rr_dir = os.path.join(path, "embedder"), os.path.join(path, "reranker")
+
         # embedder = bi-encoder; holds the canonical shared word-embedding table
-        self.embedder = SentenceTransformer(emb_dir, device=self.device)
+        self.embedder = SentenceTransformer(emb_dir, device=self.device, trust_remote_code=trc)
         shared_wemb = self.embedder[0].auto_model.embeddings.word_embeddings.weight.data
-        # reranker = cross-encoder seq-classifier; word-embedding stripped on disk -> injected from the shared table
-        cfg = AutoConfig.from_pretrained(rr_dir)
-        self.reranker = AutoModelForSequenceClassification.from_config(cfg)
+
+        # reranker = cross-encoder seq-classifier; word-embedding stripped on disk -> injected from shared table
+        rr_cfg = AutoConfig.from_pretrained(rr_dir, trust_remote_code=trc)
+        self.reranker = AutoModelForSequenceClassification.from_config(rr_cfg, trust_remote_code=trc)
         self.reranker.load_state_dict(load_file(os.path.join(rr_dir, "model.safetensors")), strict=False)
-        self.reranker.bert.embeddings.word_embeddings.weight.data = shared_wemb.to(self.reranker.dtype).clone()
+        # resolve the encoder submodule generically (.bert for BERT, .roberta for XLM-R, ...)
+        base = self.reranker.base_model
+        base.embeddings.word_embeddings.weight.data = shared_wemb.to(self.reranker.dtype).clone()
         self.reranker.to(self.device).eval()
-        self.rr_tok = AutoTokenizer.from_pretrained(rr_dir)
+        self.rr_tok = AutoTokenizer.from_pretrained(rr_dir, trust_remote_code=trc)
+        # Weights may be stored fp16 on disk (smaller artifact); CPU can't compute in half -> upcast to fp32.
+        if str(self.device) == "cpu":
+            self.embedder = self.embedder.to(torch.float32)
+            self.reranker = self.reranker.float()
 
     def embed(self, texts, is_query=False, batch_size=32):
-        """Return L2-normalized bi-encoder vectors. (This embedder uses no query/doc prefix.)"""
-        return self.embedder.encode(list(texts), normalize_embeddings=True,
+        """Return L2-normalized bi-encoder vectors. Applies the model's query/doc prompt if configured."""
+        prompt = self.q_prompt if is_query else self.d_prompt
+        texts = [prompt + t for t in texts] if prompt else list(texts)
+        return self.embedder.encode(texts, normalize_embeddings=True,
                                     batch_size=batch_size, show_progress_bar=False)
 
     @torch.no_grad()
@@ -52,12 +76,12 @@ class BaaMiniEmbeddingReranker:
 
 
 if __name__ == "__main__":
-    m = BaaMiniEmbeddingReranker()
+    import numpy as np
+    m = BaaEmbeddingReranker()
     q = "How does a cross-encoder reranker work?"
     docs = ["A cross-encoder jointly encodes the query and document to score relevance.",
             "The mitochondria is the powerhouse of the cell.",
             "Bi-encoders embed query and document separately for fast retrieval."]
-    import numpy as np
     qv = m.embed([q], is_query=True)[0]; dv = m.embed(docs)
     print("embed cos:", [round(float(np.dot(qv, d)), 3) for d in dv])
     print("rerank   :", [(round(s, 2), d[:45]) for d, s in m.rerank(q, docs)])