Nawah-Router-BERT-6M-v2 — موجّه عربي صفري
Give it a text and any categories in plain Arabic; it scores all of them in
one forward pass. No fixed taxonomy. 5,977,985 parameters against
Nawah-Router-v3's
52,049,409 — 8.7× smaller.
Results — it beats the 52M on three of four
All three models scored in one session by the shipped eval_router_only.py;
Router-v3 reproduced its published card (0.9305 / 0.6976 / 0.6130 / 0.9008).
| eval | Router-v3 (Llama, 52M) | BERT-6M v1 | this model, v2 | random |
|---|---|---|---|---|
| unseen category sets | 0.9308 | 0.9137 | 0.9327 | 0.2137 |
| unseen domains | 0.6975 | 0.6696 | 0.7009 | 0.2521 |
| unseen axes | 0.6127 | 0.6024 | 0.6000 | 0.2358 |
| deliberately adjacent categories | 0.9017 | 0.8924 | 0.9101 | 0.2109 |
A 6M encoder outperforms the 52M decoder on unseen category sets (+0.2), unseen domains (+0.3) and adjacent categories (+0.8), at 1/8.7 the size.
It loses on unseen axes — 0.6000 against 0.6127 — and that is the column the
original card calls its strongest claim: tools and retrieval appear nowhere
in training. Note also that v2 is worse than v1 there (0.6024), the only
metric across all three tasks where more pretraining hurt. Three of the four
margins are under one point, which is within what a single run can tell you.
Why no pooling change was needed
RouterModel pools each category's token span via bmm(cat_pool, hidden)
rather than a fixed position, so it is architecture-agnostic and a BERT backbone
drops in unchanged. The sibling
Nawah-Guard-BERT-6M-v2
needed a mean-pooling patch; this did not.
Usage
from transformers import AutoTokenizer
from routing_model import RouterModel, route # ships in this repo
M = "oddadmix/Nawah-Router-BERT-6M-v2"
tok = AutoTokenizer.from_pretrained(M)
model = RouterModel.from_pretrained(M)
route(model, tok, "كم صار سعر صرف الدولار اليوم؟",
["بحث في الويب", "حاسبة", "تقويم ومواعيد", "لا يحتاج أداة"])
Trained on oddadmix/arabic-prompt-routing (207,097 rows, 12 axes) with the
dataset's own train_router_head.py at its defaults — decoder-tuned.
© KAND CA 2026 — PROJECT NAWAH