File size: 3,004 Bytes
9f0cc33 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | ---
license: apache-2.0
language:
- ar
base_model: oddadmix/50M-2048-Emhotob
library_name: transformers
tags: [arabic, zero-shot-classification, prompt-routing, router]
---
# 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`](https://huggingface.co/datasets/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
```python
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.
|