---
language: [ar]
license: apache-2.0
library_name: transformers
base_model: oddadmix/Nawah-BERT-6M-v2
datasets: [oddadmix/arabic-prompt-routing]
tags: [arabic, zero-shot-classification, prompt-routing, router, bert, encoder, tiny-model]
---

# 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`](https://huggingface.co/oddadmix/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`](https://huggingface.co/oddadmix/Nawah-Guard-BERT-6M-v2)
needed a mean-pooling patch; this did not.

## Usage

```python
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