Nawah-Router-v3 — موجّه عربي صفري
52M parameters. Give it a text and any categories in plain Arabic; it scores all of them in one forward pass. Categories are chosen at inference — no fixed taxonomy.
بالعربية: نموذج عربي يوجّه أي نص إلى فئة من فئات تكتبها أنت بلغة طبيعية، في مسار واحد.
Results
Trained on oddadmix/arabic-prompt-routing
(233,720 rows, 12 routing axes).
| eval | v3 | v2 (51K corpus) | random |
|---|---|---|---|
| unseen category sets | 0.9305 | 0.9199 | 0.2137 |
| unseen domains | 0.6976 | 0.6665 | 0.2521 |
| unseen axes | 0.6130 | (n/a) | 0.2358 |
| deliberately adjacent categories | 0.9008 | 0.9149 | 0.2109 |
unseen_axis is the strongest claim here: tools and retrieval appear nowhere in training,
and the model still routes along them at 0.61 against a 0.24 baseline.
Usage
from transformers import AutoTokenizer
from routing_model import RouterModel, route # ships in this repo
M = "oddadmix/Nawah-Router-v3"
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"])
Choosing a checkpoint
No single configuration wins everything, and the trade-off is real:
| epochs | unseen lanes | unseen domains | unseen axes |
|---|---|---|---|
| 1 | 0.9327 | 0.6968 | 0.5950 |
| 2 (this) | 0.9305 | 0.6976 | 0.6130 |
| 3 | 0.9181 | 0.6496 | 0.6376 |
Longer training helps the hardest transfer (unseen axes) and costs the everyday cases. 2 epochs is shipped as the balance. Higher learning rates are simply worse — 6e-4 and 1e-3 both degrade.
How the head works
Text and categories share one sequence, text first. Each category's span is mean-pooled into its own vector and a shared scorer turns each into one logit; the softmax runs over the categories supplied. Because the scorer is shared it reads category content, not slot index — which is what makes the label set free text.
A fixed-slot head (num_labels = max_lanes) scored exactly 1/n at every lane count: its
weights were positional, and the corpus randomises category order, so there was nothing to learn.
Limitations
Unseen domains (0.70) and unseen axes (0.61) trail unseen category sets (0.93) — it generalises best inside verticals and dimensions it has seen. Confidence is not calibrated: clear cases saturate near 1.0, so use the ranking, not the number. Arabic, 1–3 line messages.
3 epochs at LR 3e-4 cosine, batch 32, bf16, max_length 320.