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metadata

title: Nawah Router
emoji: 🧭
colorFrom: green
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
models:
  - oddadmix/Nawah-Router-BERT-6M-v2
  - oddadmix/Nawah-Router-v3
  - oddadmix/Nawah-Router-BERT-6M-bilingual-pretrained
  - oddadmix/Nawah-BERT-6M-v2
  - oddadmix/Nawah-BERT-6M-bilingual
datasets:
  - oddadmix/arabic-prompt-routing
  - oddadmix/prompt-routing-en
tags:
  - arabic
  - english
  - zero-shot-classification
  - prompt-routing
short_description: توجيه عربي وإنجليزي صفري للفئات الحرة

Nawah-Router — Arabic + English zero-shot prompt routing

Write a text and any categories, in plain Arabic or English; the model scores all of them in a single forward pass. Categories are free text typed at runtime — the model has no fixed taxonomy, so this is zero-shot classification over a label set it has never seen. Language is detected automatically from the text.

Edit any category inline, add or remove lanes, and the routing updates as you type. Counterpart to LFM2.5-Encoder-350M-Prompt-Router.

Three backbones, one head — switch from the picker

	`Nawah-Router-BERT-6M-v2`	`Nawah-Router-v3`	`Nawah-Router-BERT-6M-bilingual-pretrained`
parameters	5,977,985	52,049,409	5,977,985
backbone	BERT encoder	Llama decoder	BERT encoder
languages	Arabic only	Arabic only	Arabic + English
base model	`Nawah-BERT-6M-v2`	`50M-2048-Emhotob`	`Nawah-BERT-6M-bilingual`

The two Arabic-only backbones, scored in one session by the same eval_router_only.py (Router-v3 reproduced its published card exactly):

eval set	6M encoder	52M decoder	random
unseen category sets	0.9327	0.9308	0.2137
unseen domains	0.7009	0.6975	0.2521
deliberately adjacent categories	0.9101	0.9017	0.2109
unseen axes (dimensions absent from training)	0.6000	0.6127	0.2358

The 6M wins three of four at 1/8.7 the size. It loses on unseen axes — the column the 52M's card calls its strongest claim — and three of the four margins are under one point, which is inside what a single run can tell you.

The bilingual 6M — same architecture, pretrained from scratch on 5B Arabic + 5B English tokens with a shared 32K tokenizer, then given a router head trained on both languages together — closes most of the English gap an Arabic-only backbone has on this task, at a small Arabic cost on the hardest split:

eval set	bilingual 6M — English	bilingual 6M — Arabic	Arabic-only 6M (Arabic)
unseen category sets	0.9246	0.9305	0.9327
unseen domains	0.6946	0.6911	0.7009
unseen axes	0.6594	0.5697	0.6000
deliberately adjacent categories	0.9066	0.9101	0.9101

Full 3-way comparison, including the finding that an English-only router head on the Arabic-only backbone learns nothing at all, in the model card.

Confidence is not calibrated: clear cases saturate near 100%. Use the ranking, not the number.

Data: oddadmix/arabic-prompt-routing (207,097 training rows, 58,008 distinct category sets, 12 routing axes) and its English counterpart oddadmix/prompt-routing-en (173,223 training rows) — both natively generated per language, not translations of each other, with the full generation and verification pipeline.

CPU-only, one forward pass regardless of how many categories you give it.