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AAC Pictogram Retriever (pt-BR)

A compact Brazilian-Portuguese embedding model that finds the right ARASAAC pictogram for a short search term — built for Augmentative and Alternative Communication (AAC). It powers pictogram search in Papuguinho, a tool for building communication boards.

~157M parameters, runs comfortably on CPU (no GPU).
One model, two jobs: retrieval (find the best pictogram among ~14k) and reranking (order a shortlist).
Competitive with much larger cloud embedding APIs on this domain, while being open and self-hostable.
Ships with a ready-to-serve GGUF build for llama.cpp: aac-pictogram-retriever-ptbr-GGUF (~190 MB RAM at Q8).

Model family & resources

Resource	Description
🧠 aac-pictogram-retriever-ptbr	This model — retrieval + reranking, 157M, CPU
⚡ aac-pictogram-retriever-ptbr-GGUF	GGUF quants for `llama.cpp` (Q4/Q6/Q8/f16)
📊 aac-pictogram-retrieval-ptbr	Evaluation benchmark (corpus / queries / qrels)
🏋️ aac-pictogram-retrieval-ptbr-training	Training data (retrieval pairs + graded reranking)
🌱 colibri-embed-ptbr	Base pt-BR embedding model

What it's for

In AAC, a child or therapist searches for a word (e.g. "escovar os dentes", "quero água") and the app must surface the pictogram that best represents it, so the child can point to it to communicate. This is a short-query, closed-domain, Brazilian-Portuguese retrieval problem where general-purpose embedders and leaderboard rankings do not transfer well. This model is tuned specifically for it.

Intended use: semantic search and reranking of ARASAAC pictograms from pt-BR words/phrases, in AAC applications.

Not intended for: general-purpose sentence embeddings, languages other than Brazilian Portuguese, or domains outside pictogram/AAC search.

Usage

With `sentence-transformers`

The model uses asymmetric prompts — one for the query, one for the document (pictogram text). Always apply them.

python

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("tardellirs/aac-pictogram-retriever-ptbr", trust_remote_code=True)

QUERY_PROMPT = "task: search result | query: "
DOC_PROMPT   = "title: none | text: "

query = "escovar os dentes"
# Pictogram "documents": a short text per pictogram (labels + synonyms + keywords + ARASAAC tags)
docs = [
    "escovar os dentes; escovação. tags: hygiene, health, routine",
    "escova de dentes; escova. tags: hygiene, object",
    "pente; pentear. tags: hygiene, object",
]

q = model.encode(QUERY_PROMPT + query, normalize_embeddings=True)
d = model.encode([DOC_PROMPT + t for t in docs], normalize_embeddings=True)
scores = d @ q                      # cosine similarity
print(docs[int(np.argmax(scores))]) # -> the escovar-os-dentes pictogram

Pre-compute the document embeddings for your pictogram catalog once; at query time you only embed the query and take cosine similarities. The same cosine score is used both to retrieve (over the full catalog) and to rerank a candidate shortlist.

On CPU with `llama.cpp` (recommended for deployment)

Use the GGUF build — it serves the full pipeline (pooling + dense projections) and loads in ~190 MB at Q8:

bash

# from tardellirs/aac-pictogram-retriever-ptbr-GGUF (keep tokenizer.model next to the .gguf)
llama-server -m aac-Q8_0.gguf --embedding --pooling mean --host 127.0.0.1 --port 8080
# POST {"input": "task: search result | query: escovar os dentes"} to /v1/embeddings ; then cosine + normalize

Quantization quality (cosine vs. the fp16 reference, near-lossless): Q8_0 0.9998, Q6_K 0.9991, Q4_K_M 0.9926.

Model details

Architecture: SentenceTransformer — Transformer → mean Pooling → Dense (768→3072) → Dense (3072→768) → Normalize. Output dimension 768.
Base model: tardellirs/colibri-embed-ptbr, a Brazilian-Portuguese model derived from Google's EmbeddingGemma-300M with its vocabulary trimmed to the most relevant pt-BR tokens (~157M parameters after trimming). Architecture and prompts are inherited from EmbeddingGemma.
Matryoshka embeddings: trained with nested dimensions [768, 256, 128], so outputs can be truncated to 256 or 128 dims for cheaper storage/search with graceful quality loss.
Prompts: query task: search result | query: , document title: none | text: .

Training

Training data is released as aac-pictogram-retrieval-ptbr-training. The model was adapted to the AAC pictogram domain by distilling relevance signal from a strong LLM judge over the ARASAAC catalog:

Retrieval supervision: for each pictogram, an LLM generated varied pt-BR search terms a child, therapist or caregiver might type, giving broad coverage of the full catalog (query → pictogram pairs).
Rerank supervision: for realistic communication-board contexts, an LLM assigned graded relevance scores to candidate pictograms per target word, providing both positives and hard negatives.
Objective: contrastive retrieval loss (in-batch negatives) + a graded-distillation margin loss, wrapped in a Matryoshka loss over [768, 256, 128]. Documents are represented by a single text per pictogram (labels + synonyms + keywords + ARASAAC tags), so one embedding per pictogram serves both retrieval and reranking.

Evaluation

Evaluated on the aac-pictogram-retrieval-ptbr benchmark — a held-out set of genuinely unseen, non-canonical pt-BR queries (morphological variants, child language, synonyms) against the full ~14k-pictogram catalog — a deliberately hard generalization test. Relevance was judged by an LLM over a candidate pool pooled across all compared systems (TREC-style pooling — so newcomers are not penalized for retrieving un-judged pictograms). Recall@k over the 13,800-pictogram corpus, sorted by R@5:

#	System	R@1	R@5	R@10	Size / type
1	gemini-embedding-001	0.542	0.797	0.864	cloud API
2	aac-pictogram-retriever-ptbr (this model)	0.546 🥇	0.786	0.827	157M · CPU
3	text-embedding-3-large	0.472	0.756	0.845	cloud API
4	qwen3-embedding-4b	0.480	0.745	0.812	4B
5	voyage-4	0.447	0.716	0.823	cloud API
6	text-embedding-3-small	0.347	0.594	0.675	cloud API
7	voyage-4-lite	0.354	0.583	0.712	cloud API
8	gte-large	0.229	0.424	0.505	335M
9	e5-base-v2	0.236	0.406	0.528	110M
10	gte-base	0.207	0.391	0.502	110M
11	all-MiniLM-L12-v2	0.126	0.336	0.406	33M
12	paraphrase-MiniLM-L6-v2	0.096	0.258	0.354	22M

On the reranking task (choosing among a candidate shortlist), the model reaches accuracy@loose ≈ 0.96 with a false-accept rate ≈ 0.04 ("better to omit than to show the wrong picture" — an important property for AAC).

Takeaway: a 157M open model running on CPU ranks #2 of 12 on this domain — it has the best R@1 of all systems (edging gemini-embedding-001), and on novel concepts (the target split) it ranks #1 by R@5 (0.806 vs. gemini's 0.778). It outranks much larger cloud APIs and multi-billion-parameter models (text-embedding-3-large, qwen3-embedding-4b, voyage-4, text-embedding-3-small). Only gemini-embedding-001 edges it, by ~1 pt of R@5 — and the LLM judge is from the same family as that model. A fully self-hostable model at a tiny fraction of the size and cost.

Limitations & responsible use

Domain-specific. Tuned for ARASAAC pictogram search in Brazilian Portuguese. It is not a general-purpose embedder and should not be assumed to transfer to other languages, catalogs, or tasks.
LLM-distilled supervision and evaluation. Training labels and the benchmark above use LLM-judged relevance, not human gold annotations. Numbers are best read as relative comparisons on an internal benchmark; for clinical or high-stakes deployments, validate against human judgments in your setting.
AAC is assistive technology used by vulnerable users (often non-verbal children). Keep a human in the loop: present pictograms for a person to confirm, and prefer omitting an item over showing a wrong one.

License & attribution

This model is released under the Gemma license, inherited from its EmbeddingGemma base. Your use must comply with the Gemma Terms of Use and Prohibited Use Policy.
ARASAAC pictograms (which this model is trained to retrieve) are authored by the Government of Aragón and distributed under CC BY-NC-SA. The model operates on text about pictograms; if you distribute pictogram content, follow ARASAAC's attribution and non-commercial terms.

Citation

Developed for and used in Papuguinho — www.papuguinho.com.

bibtex

@software{aac_pictogram_retriever_ptbr,
  title  = {AAC Pictogram Retriever (pt-BR)},
  author = {Stekel, Tardelli R. C. and Antonelli, Manuela and Baroni, Gabriel P. and {Carvalho Neto}, José A. and Souza, Marcela J. V.},
  year   = {2026},
  url    = {https://huggingface.co/tardellirs/aac-pictogram-retriever-ptbr},
  doi    = {10.57967/hf/9658},
  publisher = {Hugging Face},
  note   = {A CPU-friendly Brazilian-Portuguese embedding model for ARASAAC pictogram retrieval and reranking, used in Papuguinho (www.papuguinho.com)}
}

tardellirs/aac-pictogram-retriever-ptbr