Dihya-22M

A 22.0M-parameter (21,999,616 published) byte-level Conv-Transformer that upgrades Dihya-5M across the entire Berber dialect continuum in both Latin and Neo-Tifinagh scripts, North African Arabic dialects in Arabic script and Latin Arabizi, Modern Standard Arabic, and major world languages: twenty-two classes across five scripts.

On a stratified held-out set of 14,247 sentences across 36 (language, domain) cells it reaches 91.90% accuracy and 0.9180 macro-F1 over all twenty-two classes. Every single class achieves $\ge 71.4%$ recall, and its worst group recall is 63.33% on the scarcest Shawiya cell.

Against Dihya-5M, it expands the inventory from seven classes to twenty-two, adds Neo-Tifinagh, Algerian Darija, Moroccan Darija, and Arabizi, lifts overall accuracy from 85.52% to 91.90%, and resolves the two central weaknesses of the 5M checkpoint:

1. Kabyle precision rises from 0.626 to 0.900 while keeping recall at 0.925. Dihya-5M traded precision for recall, misrouting 37% of its Kabyle predictions to sibling text; Dihya-22M ends that trade-off.
2. Central Atlas Tamazight recall rises from 0.660 to 0.948, and its confusion into Kabyle drops from 16.0% down to 2.58%. Tarifit confusion drops from 6.7% to 1.38%, and Tamasheq from 7.3% to 0.16%. Across Neo-Tifinagh varieties, confusion to Kabyle is $\le 2.12%$ (0.00% for Tamazight, Tarifit, and Tamasheq).

It ships with a split-conformal quantile ($\hat{q} = 0.9526$ at 99.0% nominal coverage, reaching 98.28% empirical coverage), so it returns a set of labels rather than one when text lies on the continuum and carries no distinguishing marker.

The task

Language identification across the Berber dialect continuum and North Africa confronts three structural challenges:

1. Morphological discrimination over shared roots: Amazigh varieties share substantial core vocabulary; distinguishing evidence is predominantly affixal, phonotactic, and clitic. Short sentences lacking dialect-specific markers are genuinely ambiguous.
2. Upstream corpus contamination: Broad-coverage classifiers (such as NLLB's lid218e and GlotLID) fail on this continuum by collapsing sibling varieties into Kabyle—mislabelling 91.3% of Shawiya, 80.7% of Tarifit, 79.3% of Tashelhit, and 73.3% of Central Atlas Tamazight. Unfiltered web and parallel corpora inherit this silent contamination.
3. Multi-script and dialect boundaries: Written Berber alternates between Latin and Neo-Tifinagh orthographies, while North African Arabic spans Arabic script and Latin-digit Arabizi alongside close dialectal proximity between Algerian (arq) and Moroccan (ary) Darija.

Results

14,247 sentences across 36 (language, domain) cells, held out completely disjoint from train (113,974) and dev (7,145).

Against the public identifiers and Dihya-5M

All systems scored in one pass over the held-out test rows:

Public identifiers cannot name Shawiya, Central Atlas Tamazight, Tarifit, Tumzabt, or Arabizi. When asked to classify texts in these varieties, their accuracy is zero by construction.

The quantity that matters downstream is how much sibling text ends up labelled Kabyle, since that is what a filter keyed on kab silently ingests:

Central Atlas Tamazight leakage into Kabyle drops sixfold from Dihya-5M's 16.0% down to 2.58%, and Tamasheq drops from 7.3% to 0.16%. In Tifinagh, cross-leakage into Kabyle is virtually zero.

Per class, all twenty-two

The twenty-two-class report, which is what test-report.json holds:

Kabyle precision is 0.900, up from 0.626 in Dihya-5M. Dihya-5M traded precision for recall, sending 37% of its Kabyle predictions to sibling varieties. Dihya-22M resolves that tension: Kabyle precision reaches 0.900 while maintaining 0.925 recall.

Central Atlas Tamazight recall rises from 0.660 to 0.948, resolving the largest weakness of the 5M checkpoint.

Shawiya recall reaches 0.714 on 98 test rows, against 0.687 in Dihya-5M, with precision rising to 0.864. 447 training rows is what exists in machine-readable form.

Arabic dialect discrimination

Separating Algerian Darija (arq), Moroccan Darija (ary), and Modern Standard Arabic (ara) in Arabic script and Arabizi:

Arabizi and Arabic script have zero cross-leakage (0 errors). Over 89.8% of Algerian Darija and 78.1% of Moroccan Darija are separated correctly; on shared Maghrebi idioms the conformal set emits ('arq_Arab', 'ary_Arab').

By (language, domain) cell

Reporting is on 34 active (language, domain) cells with support $\ge 20$:

The lowest cells with support $\ge 20$:

The Shawiya cells reflect the language's digital scarcity. ary_Arab/negative_dialect holds Moroccan colloquial text sharing vocabulary with western Algerian dialects. zgh_Tfng and shi_Tfng share everyday lexicon in standard Neo-Tifinagh orthography.

Conformal prediction sets

Calibrated by split conformal at target coverage 99.0%. Fitted on one half of the development set (3,551 sentences) and coverage measured on the other (3,551 held-out sentences):

Usage

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(
    "agbalu/Dihya-22M", trust_remote_code=True
)

for row in model.identify([
    "Azul fell-awen, amek tettilim ass-a?",
    "Mayemmi ur teccid ticemrarin nnecm a gma?",
    "Bonjour tout le monde, comment allez-vous?",
    "ⵜⴰⵇⴱⴰⵢⵍⵉⵜ ⴷ ⵜⵓⵜⵍⴰⵢⵜ ⵜⴰⵢⴻⵎⵎⴰⵜ ⵏⵏⴻⵖ.",
    "Hello everyone, how are you doing today?",
    "صباح الخير كيداير لاباس عليك",
    "واش راك خويا لاباس؟",
]):
    print(row["language"], round(row["confidence"], 4), row["prediction_set"])
# kab_Latn 0.5295 ('kab_Latn', 'shy_Latn')
# rif_Latn 0.5485 ('kab_Latn', 'rif_Latn')
# fra_Latn 0.9444 ('fra_Latn',)
# kab_Tfng 0.9265 ('kab_Tfng',)
# eng_Latn 0.9498 ('eng_Latn',)
# ary_Arab 0.8180 ('arq_Arab', 'ary_Arab')
# arq_Arab 0.4889 ('arq_Arab', 'ary_Arab')

identify applies the conformal quantile that the numbers above were measured with; it travels in config.json. The plain forward returns raw cosine logits.

The tokenizer is the 256 UTF-8 byte values and is only needed for batching:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("agbalu/Dihya-22M")
model = AutoModelForSequenceClassification.from_pretrained(
    "agbalu/Dihya-22M", trust_remote_code=True
)
batch = tokenizer(["Azul fell-awen", "صباح الخير"], return_tensors="pt", padding=True, truncation=True)
logits = model(**batch).logits

Prediction sets widen where text is genuinely ambiguous. The greeting above returns ('kab_Latn', 'shy_Latn') — Shawiya and Kabyle share that greeting and neither carries a marker separating them. A forced singleton there would be an arbitrary guess.

Intended use

Screening and routing text in corpus pipelines: tagging web scrapes and OCR output across Latin, Tifinagh, and Arabic scripts, discarding non-target lines, and routing input to variety-specific models. Use the prediction set rather than the argmax where a wrong label would corrupt a corpus.

Not suitable for distinguishing sub-varieties inside any of the twenty-two classes, single isolated words lacking morphological context, or attributing authors on the basis of dialect.

Architecture

The published model excludes the auxiliary script classification head, binary filter head, and contrastive projection head (198,912 parameters), which shape representations during training and are never read at inference.

Four kernel widths because evidence sits at four physical scales: grapheme clusters and diacritics ($k=3$), inflectional affixes ($k=5$), clitic chains and annexation markers ($k=7$), and word stems ($k=11$).

Training

Five objectives address long-tail imbalance, domain collapse, and multi-script structure:

1. LDAM margins for the long tail — $m_k = C / n_k^{1/4}$ (Cao et al., NeurIPS 2019).
2. Convex Group-DRO — exponentiated-gradient weights over the active (language, domain) groups (Sagawa et al., ICLR 2020) with convex normalization.
3. Domain-aware supervised contrastive learning — negatives mined from a different language in the same domain first (ConLID) over a 4,096-entry memory bank.
4. Multi-task auxiliary heads — script classification (5 scripts) and binary Amazigh filtering.
5. Deferred schedule — margins and group weights are disabled for the first half of training (steps 0–2,376).

Selection is on the mean of dev macro-F1 and dev worst-group recall, never on loss.

Limitations

Dialect continuum ambiguity: Short sentences of shared vocabulary carrying no variety-specific morphology cannot be deterministically resolved to a single class. The conformal prediction set captures this ambiguity directly by returning a multi-label set.

Shawiya data scarcity: 447 training rows and 98 test rows represent the entirety of machine-readable Shawiya text currently available; reported metrics reflect these sample size bounds.

Maghrebi Arabic dialect overlap: Algerian and Moroccan Arabic share substantial colloquial syntax and lexicon, leading to cross-dialect conformal sets on shared idioms.

Input length: Evaluated on sentence-level text; single isolated words lack sufficient phonotactic context for reliable attribution.

Code-switching: Outputs sentence-level classifications; token-level language diarization is not supported.

Files

No optimiser state, no scheduler state, no RNG. Training cannot be resumed from these files.

The name

Dihya (also called al-Kāhina, died c. 703 CE) was an Amazigh military leader from the Aurès Mountains of what is now eastern Algeria — the Shawiya homeland — who led the Berber resistance. The naming is homage and implies no endorsement by anyone.

Citation

@software{agbalu_dihya_22m_2026,
  title  = {Dihya-22M: Pan-Amazigh, North African Arabic, and World Language Identification},
  author = {AƔBALU},
  year   = {2026},
  url    = {https://huggingface.co/agbalu/Dihya-22M},
  note   = {22M parameters; 91.90\% accuracy over 22 classes on 14,247 held-out sentences}
}

Licence

Apache-2.0 on the weights and the code. The training corpus is assembled from sources under mixed licences; see resources/sibling_registry.yaml and resources/corpus_registry.yaml for per-source terms.

Part of AƔBALU, a Kabyle and Amazigh corpus and model collection.