Jisr-MT-50M-AllDialects

Jisr-MT-50M-AllDialects

One 49M model, English → 13 Arabic dialects + MSA, selected by tag.

Trained to test whether dialects share enough structure to help each other. They largely do — it beats the single-dialect models on 5 of 9 comparable dialects and ties 2 more, at 1/13th the storage.

Results

UBC-NLP/alexandria test splits, beam 4. "single" is the dedicated 49M model for that dialect.

¹ vs Jisr-MT-50M-Masri-v2, the combined-corpus model.

Sudanese and Tunisian flip sign between the two metrics, so they are genuine ties. The honest tally is 5 clear wins, 3 clear losses, 2 ties.

The pattern is data-size dependent. Gains concentrate in small-corpus dialects — Libyan (12,850 pairs, +7.62) and Palestinian (29,283, +4.98) — and losses in the large ones: Moroccan (155,939) and Egyptian (which also has a second corpus). Dialects with enough data of their own are diluted by sharing capacity; dialects without enough are rescued by it.

MSA retention — the best in the family

It saw 13 dialects' worth of MSA replay rather than one, so >>ara<< received proportionally more gradient.

Compared with a frontier model

gpt-5.1 on the same benchmark (200 turns per dialect, prompted to write dialect not MSA):

gpt-5.1 leads on 9 of 10, by 2.9–13.3 chrF++, averaging 39.21 against this model's ~34. The exception is Moroccan, where gpt-5.1 collapses to 20.86 — less than half its Saudi score — and this model leads by 9.64. See Jisr-MT-50M-Moroccan for that result.

gpt-5.1 also gains +2.3 to +4.8 chrF++ from orthographic normalisation while Jisr models gain ~+1.6, so the raw gaps understate it: trained in-domain, these models already match the references' spelling conventions.

Usage

from transformers import AutoTokenizer, MarianMTModel
tok = AutoTokenizer.from_pretrained("oddadmix/Jisr-MT-50M-AllDialects")
model = MarianMTModel.from_pretrained("oddadmix/Jisr-MT-50M-AllDialects")

def tr(text, tag):
    return tok.decode(model.generate(**tok(f"{tag} {text}", return_tensors="pt"),
                                     num_beams=4)[0], skip_special_tokens=True)

tr("Where are you going?", ">>arz<<")   # Egyptian
tr("Where are you going?", ">>ary<<")   # Moroccan
tr("Where are you going?", ">>ara<<")   # MSA

The tag is required — the model cannot infer the target dialect. English → Arabic only.

Note >>apc<< (Lebanese) and >>ajp<< (Syrian) deviate from strict ISO 639-3, where both Levantine variants share apc. They were given distinct tags so the model can separate them.

Training

Limitations

• Higher degeneracy than the single models — up to 2.6% of outputs on Sudanese repeat themselves, vs ~1% for the dedicated models. Check outputs before shipping.
• Algerian, Bahraini and Iraqi are unvalidated. Alexandria has no config for them, so 3 of the 13 tags have no external benchmark at all.
• Dialect data is Gemma-translated ASR transcripts; for Egyptian, where a purpose-built parallel corpus exists, that corpus alone trains a better model.
• No standard orthography for any of these dialects — all chrF/BLEU here are depressed by that.
• English → Arabic only. 49M parameters.