Nawah-Router-BERT-6M-bilingual-pretrained — router head on a genuinely bilingual backbone
Same task as Nawah-Router-BERT-6M-bilingual
— give it a text and any categories in English or Arabic, it scores all of them in one forward
pass — but the backbone underneath is
Nawah-BERT-6M-bilingual, pretrained
from scratch on 5B Arabic + 5B English tokens with a tokenizer built for both languages, instead
of an Arabic-only-pretrained backbone with a bilingual router head bolted on top.
The comparison this exists to make
Three backbones, same router-head code, same two datasets, scored in one session:
| eval split | Arabic-only backbone,English-only router | Arabic-only backbone,bilingual router | this model |
|---|---|---|---|
| en_unseen_lanes | 0.210 (chance) | 0.859 | 0.925 |
| en_unseen_domain | 0.239 (chance) | 0.590 | 0.695 |
| en_unseen_axis | 0.252 (chance) | 0.547 | 0.659 |
| en_hard | 0.213 (chance) | 0.839 | 0.907 |
| ar_unseen_lanes | — | 0.929 | 0.931 |
| ar_unseen_domain | — | 0.694 | 0.691 |
| ar_unseen_axis | — | 0.605 | 0.570 |
| ar_hard | — | 0.903 | 0.910 |
Pretraining the backbone bilingually — not just fine-tuning the head bilingually — closes most
of the English gap. Every English split gains 6–11 points over the Arabic-only backbone. Arabic
holds flat on three of four splits, with one real cost: unseen_axis (tools/retrieval, held out
of training entirely) drops 0.605 → 0.570. That's consistent with the tokenizer trade-off behind
the new backbone — Arabic went from 1.20 to 1.37 tokens/word to make room for English — and with
the backbone splitting fixed capacity across two languages rather than devoting all of it to one.
Row 1 is still the headline finding underneath all of this: an Arabic-only-pretrained backbone with an English-only router head learns nothing at all (flat loss, chance accuracy). Both fixes here — a bilingual router head, or a bilingual-pretrained backbone — depend on Arabic data being present somewhere in the pipeline.
Usage
from transformers import AutoTokenizer
from routing_model_bilingual import RouterModel, route # ships in this repo
M = "oddadmix/Nawah-Router-BERT-6M-bilingual-pretrained"
tok = AutoTokenizer.from_pretrained(M)
model = RouterModel.from_pretrained(M)
route(model, tok, "The order is an hour late and the driver isn't answering",
["delivery enquiry", "delayed-order complaint", "payment issue"])
# lang="en"/"ar"/"auto" (default) - auto picks by script
Trained on oddadmix/prompt-routing-en (173,223 rows) + oddadmix/arabic-prompt-routing
(207,097 rows) mixed in one run, 3 epochs, train_router_head_multi.py defaults (LR 3e-4, batch
32). Each row wrapped in its own language's template before the shared span-pooling head scores
it — see that script's docstring.
© KAND CA 2026 — PROJECT NAWAH