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| title: README |
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| # AƔBALU |
|
|
| **Aɣbalu** (ⴰⵖⴱⴰⵍⵓ) is Kabyle for *the source* — a spring, the place water comes from. |
|
|
| This organization builds NLP resources for **Kabyle** (Taqbaylit, `kab`, Latin script), a |
| Northern Berber language of Kabylia, Algeria, spoken by 5–7 million people. Kabyle is |
| unusual among low-resource languages: it has a *disproportionately large* speech resource |
| and a thin, noisy text resource. The entire clean Kabyle web crawl is smaller than a single |
| Tatoeba export. |
|
|
| The first artifact is not a model. It is the corpus. |
|
|
| ## Models |
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| | | | | |
| |---|---|---| |
| | [**Amrouche-1.3B**](https://huggingface.co/agbalu/Amrouche-1.3B) | translation, 1.3B | Beats NLLB-200-1.3B in **all four directions**, and by the widest margin into Kabyle: **36.34 chrF++** and **10.86 BLEU** on English→Kabyle, against NLLB's own published 6.2 BLEU for the language | |
| | [**Masinissa-31M**](https://huggingface.co/agbalu/Masinissa-31M) | encoder, 31M | **90.51%** on gold UD part-of-speech tags — against 83.42% for a most-frequent-tag baseline and 63.69% for the previously published Kabyle tagger | |
| | [**Mammeri-Tok**](https://huggingface.co/agbalu/Mammeri-Tok) | tokenizer | Ten Unigram vocabularies, 8k–32k, two initialisation arms. The sweep settles two open questions about Kabyle segmentation | |
|
|
| Still to come: speech recognition, voice, and a generative model. Each is named for a figure |
| whose life was that model's work — see below. |
|
|
| ## Datasets |
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|
| | | | | |
| |---|---|---| |
| | [**KabBench**](https://huggingface.co/datasets/agbalu/KabBench) | evaluation | A **repaired** Kabyle MT reference — 326 of its 2,009 sentences were corrupt in the public original and have never been fixed upstream — plus a balanced six-language set for telling Kabyle from its Berber siblings | |
| | [**KabLex**](https://huggingface.co/datasets/agbalu/KabLex) | lexicon | 366,892 lexical entries over 17,090 lemmas, with morphological features, and 25,642 word–pronunciation pairs in IPA | |
|
|
| **Every Kabyle BLEU score ever published is measured against a broken reference.** 16.2% of |
| the Kabyle side of the standard MT benchmark carries homoglyph corruption, so a system that |
| spells Kabyle correctly is penalised for it. `KabBench` is that reference, repaired. |
|
|
| Both are licence-cut by code rather than by hand: `KabLex` publishes 366,892 of 395,834 |
| entries, excluding a share-alike source and one whose licence could not be established, and |
| every row carries its own `source` and `licence` so a stricter subset can be cut downstream. |
|
|
| ## What makes these different |
|
|
| **The orthography is repaired, not ignored.** Public Kabyle text carries systematic homoglyph |
| corruption: Greek `ε` U+03B5 standing in for Latin `ɛ` U+025B, `γ` for `ɣ`, in 2.6–3.2% of |
| rows of the largest sources. Published Kabyle tokenizers have baked this into their merges at |
| a measured cost of **+17.8% to +21.3% tokens** on correctly spelled text. Every artifact here |
| passes a versioned 81-rule normaliser first, and every artifact is stamped with its version. |
|
|
| That repair shows up downstream as measurement rather than as a claim, twice. Under deliberate |
| homoglyph corruption of its input, Masinissa-31M still scores **87.42%** — above every |
| baseline's *clean* number. And Amrouche-1.3B's score is **identical** whether or not both sides |
| are normalised before scoring — a gap of 0.00 into Kabyle, where the untuned base model gains |
| 0.17–0.19 — so the model has learned to spell Kabyle canonically. |
|
|
| **Provenance is a precondition, not a footnote.** Every sentence in the training corpus |
| carries a source id, a licence, and a retrieval date. A record whose origin cannot be named |
| does not enter. The licence composition of the training text is published on every model |
| card, including the uncomfortable part: **34.9% of it has no licence anyone could resolve.** |
| A permissive grant on weights makes no claim about the text underneath them, and saying so |
| is cheaper than pretending otherwise. |
|
|
| **Evaluation reports what it measures.** FLORES+ `kab_Latn`, the benchmark every published |
| Kabyle result is scored against, is **16.2% orthographically corrupt and has never been |
| revised upstream** — which means 2.71 BLEU on its devtest is unreachable by a system that |
| spells Kabyle correctly. Results here are scored twice, as-published and with both sides |
| normalised, and the difference is reported. A published accuracy is a claim about a dataset, |
| not about a task: one Kabyle model card's 94.8% is agreement with the lexicon projection that |
| generated its own labels; on gold annotation it is 63.69%. |
|
|
| **Decontamination is measured, with a positive control.** Zero overlap between the training |
| corpus and FLORES+ or SIB-200. Both derive from Wikipedia, which is in the corpus, so this |
| was checked rather than assumed. |
|
|
| ## The names |
|
|
| Each model is named for someone whose life was the work that model does, in two families. |
|
|
| | model | does | name | | |
| |---|---|---|---| |
| | tokenizer | decides how Kabyle is written down | **Mammeri** | Mouloud Mammeri wrote the first Berber grammar written *in* Kabyle (1976), inventing the metalanguage to do it. The 1980 cancellation of his lecture on Kabyle poetry began the Berber Spring | |
| | encoder | understands; one representation | **Masinissa** | first king of a united Numidia, r. 202–148 BCE — he brought the eastern and western tribes into one kingdom, as the encoder brings 42 sources into one representation | |
| | translation | carries Kabyle out and back | **Amrouche** | Taos Amrouche sang exclusively in Kabyle and wrote in French — a life spent carrying one into the other | |
| | speech recognition | hears Kabyle, writes it down | **Fadhma** | her mother, Fadhma Aït Mansour Amrouche, who from 1930 began writing down the songs and tales inherited from her ancestors | |
| | voice | speaks Kabyle | **Matoub** | Matoub Lounès — the voice | |
| | generative | generates; sovereign | **Jugurtha** | Masinissa's grandson, who fought Rome 111–104 BCE and was never taken in battle | |
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|
| Two lineages carry the design. **Masinissa and Jugurtha are grandfather and grandson**, so |
| the smallest model and the largest are the founding of the kingdom and the refusal to |
| surrender it. **Fadhma and Taos Amrouche are mother and daughter**, and they split the |
| oral-tradition work exactly as their two models do: the mother heard the songs and wrote them |
| down, which is speech recognition, and the daughter carried them into French while never |
| singing in anything but Kabyle, which is translation. One of Taos's albums is called *Chants |
| sauvés de l'oubli* — songs saved from oblivion. |
|
|
| Mammeri and Matoub stand alone, because what each of them did nobody shared: the grammar, and |
| the voice. |
|
|
| The naming is homage. It implies no endorsement by anyone, and none of these figures or their |
| families are affiliated with this work. |
|
|
| ## Licence |
|
|
| Weights are **Apache-2.0**. The text they were trained on is not, and cannot be relicensed by |
| that grant — each model card publishes the full composition so you can judge for yourself. |
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|
