--- language: - ig - ha - yo - en library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - embeddings - sentence-transformers - cross-lingual - multilingual - igbo - hausa - yoruba - information-retrieval - semantic-search base_model: sentence-transformers/LaBSE ---

Native-Bird: an Igbo query and English documents in one shared semantic vector space

## Table of Contents 1. [Introduction](#1-introduction) 2. [Model Summary](#2-model-summary) 3. [Model Downloads](#3-model-downloads) 4. [Evaluation Results](#4-evaluation-results) 5. [Quick Start](#5-quick-start) 6. [Searching Real Document Collections](#6-searching-real-document-collections) 7. [Limitations](#7-limitations) 8. [Citation](#8-citation) 9. [Contact](#9-contact) ## 1. Introduction **Native-Bird** is a 768-dimensional multilingual sentence-embedding model fine-tuned from **LaBSE** for cross-lingual retrieval across **Igbo, Hausa, Yoruba and English**. Its primary use case is **native-language query → English document retrieval**: a user describes what they are looking for in Igbo, Hausa or Yoruba, the indexed documents are written in English, and Native-Bird maps both into the same embedding space for semantic search. > **Igbo query:** “Neuklọs ahụ bụ ahụ ihe abụọ mejupụtara ya - neutrọn na protọn.” > > **English document:** “The nucleus consists of two particles - neutrons and protons.” Native-Bird is the text backbone of [**Native-Bird2**](https://huggingface.co/Modularcomputing/Native-Bird2), which extends the same space to images. ## 2. Model Summary **Architecture** | | | |---|---| | Architecture | SentenceTransformer (LaBSE encoder → pooling → dense → normalize) | | Base model | [`sentence-transformers/LaBSE`](https://huggingface.co/sentence-transformers/LaBSE) | | Embedding dimension | 768 | | Max sequence length (training) | 128 tokens | | Similarity | Cosine | | Languages | Igbo (`ig`), Hausa (`ha`), Yoruba (`yo`), English (`en`) | **Training** | | | |---|---| | Objective | Cached Multiple Negatives Ranking Loss (CachedMNRL) | | Training pairs | 76,150 native-language ↔ English pairs | | Epochs | 4 | | Per-device batch size | 256 (CachedMNRL mini-batch 128) | | Learning rate / schedule | 2e-5, cosine, 10% warmup | | Negatives | In-batch, NO_DUPLICATES sampling | | Precision | BF16 | ## 3. Model Downloads

## 4. Evaluation Results Cross-lingual retrieval against an English corpus of **1,004 sentences**, with **204 queries per language**. Each query has one known matching English sentence. Two query variants are tested: **clean** (original text) and **ASR-style noise** (diacritics removed, lowercased, punctuation stripped, whitespace normalized).

| **Language** | **Query** | **LaBSE R@1** | **Native-Bird R@1** | **LaBSE MRR** | **Native-Bird MRR** | | :--- | :--- | :---: | :---: | :---: | :---: | | Igbo | Clean | 1.0000 | **1.0000** | 1.0000 | **1.0000** | | Igbo | ASR-style noise | 0.9853 | **1.0000** | 0.9888 | **1.0000** | | Hausa | Clean | 1.0000 | **1.0000** | 1.0000 | **1.0000** | | Hausa | ASR-style noise | 0.9951 | **1.0000** | 0.9975 | **1.0000** | | Yoruba | Clean | 0.9804 | **0.9804** | 0.9871 | **0.9902** | | Yoruba | ASR-style noise | 0.9559 | **0.9804** | 0.9683 | **0.9867** |

Native-Bird reaches R@10 = 1.0000 in every setting (LaBSE: 0.9951 on noisy Igbo and Yoruba). Fine-tuning mainly improves **robustness to transcription-style noise**, which matters for voice and keyboard input without diacritics. **Direct Igbo → English retrieval examples** | Igbo query | Top English result | Cosine | | :--- | :--- | :---: | | Mgbanwe n'ọdịdị na-agbakwunye ọdịdị kejenetiki ọhụrụ, ma nhọpụta na-ewepụ ya n'ogwu ọdịdị nke a kọwapụtara. | Mutation adds new genetic variation, and selection removes it from the pool of expressed variation. | 0.7079 | | Neuklọs ahụ bụ ahụ ihe abụọ mejupụtara ya - neutrọn na protọn. | The nucleus consists of two particles - neutrons and protons. | 0.7049 | | Enwere ọtụtụ ihe mere ha ji kara web prọgzi mma: ha na-agbanwe ụzọ trafik ịntanetị niile, ọ bụghị naanị http. | They are superior to web proxies for several reasons: They re-route all Internet traffic, not only http. | 0.6774 | ## 5. Quick Start ```bash pip install -U sentence-transformers torch ``` ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("Modularcomputing/Native-Bird") query = "Neuklọs ahụ bụ ahụ ihe abụọ mejupụtara ya - neutrọn na protọn." documents = [ "The nucleus consists of two particles - neutrons and protons.", "The liver is an organ responsible for many metabolic functions.", "Photosynthesis converts light energy into chemical energy.", ] q = model.encode(query, normalize_embeddings=True) d = model.encode(documents, normalize_embeddings=True) scores = d @ q for i in scores.argsort()[::-1]: print(f"{scores[i]:.4f} {documents[i]}") ``` The matching English sentence should rank first. ## 6. Searching Real Document Collections Do **not** embed a multi-page document as one vector. Split each document into passages of roughly 100–300 words with overlap, and store each passage embedding with its parent document ID. ```text Igbo / Hausa / Yoruba query ↓ Native-Bird encoder 768-d query vector ↓ cosine similarity (FAISS / vector DB) English passage index ↓ Top-k passages → grouped by parent document ``` **Intended applications:** native-language search over English knowledge bases, cross-lingual RAG retrieval, multilingual educational search, and search over Nigerian institutional documents. ## 7. Limitations - **Embedding model, not a translator or generator.** It represents text as vectors; it does not produce English answers. - **Sentence-level evaluation.** Trained at 128 tokens and evaluated on sentences, so long documents need chunking. Scores do not guarantee the same accuracy on arbitrary long documents. - **Domain shift.** Government, legal, medical and technical text can differ substantially from the benchmark; evaluate on your target domain before production use. - **Status: research / early release.** The next validation step is a human-annotated benchmark of real Nigerian documents and real Igbo, Hausa and Yoruba information needs. ## 8. Citation ```bibtex @misc{nativebird2026, title = {Native-Bird: Cross-lingual Native-Language Embeddings for English Document Retrieval}, author = {Akuegbo, Chima Emmanuel}, year = {2026}, publisher = {Modular}, howpublished = {\url{https://huggingface.co/Modularcomputing/Native-Bird}} } ``` ## 9. Contact Questions, issues or collaboration: open a discussion on the [Community tab](https://huggingface.co/Modularcomputing/Native-Bird/discussions).