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Wouze / laya-ara
Model's Last Updated: September 24 2026
text-classification
Introduction of laya-ara
Model Details of laya-ara
laya-ara
Arabic NLU for typed decisions — intent, NLI, and short-list ranking on
laya-multilingual
.
Mohammad Alkhenizan · 21 September 2026 · LinkedIn
Hugging Face
·
GitHub
· RAG sibling:
laya-ara-rag
Highlights vs
laya-multilingual
Relative lift is (fine-tune − stock) / stock on the same frozen Laya templates.
| Arabic task | n | Base | laya-ara | Relative lift |
|---|---|---|---|---|
| Intent, 20 options (MASSIVE ar-SA) | 2974 | 0.386 | 0.816 | +111% |
| Scenario, 18-way (MASSIVE ar-SA) | 2974 | 0.427 | 0.865 | +103% |
| Hierarchical intent | 2694 | 0.536 | 0.893 | +67% |
| Offensive language, macro-F1 (OSACT4-A) | 1000 | 0.726 | 0.862 | +19% |
| Natural language inference (XNLI-ar) | 5010 | 0.686 | 0.723 | +5% |
Full tables:
RESULTS.md
. This card’s JSON:
nlu_benches.json
. All cards:
all_cards.json
.
Abstract
laya-ara
is a fine-tune of
convaiinnovations/laya-multilingual
(mmBERT-base with a Laya typed-decision head, ~322M) for Arabic
System One
inference: discrete
choice
, binary
noul
, and optional ordinal
score
. It is not a generative language model. Training uses official Laya RLCD on two RTX 3090 GPUs. The released mix is MASSIVE-ar, OSACT4-A, and a capped XNLI-ar sample. XNLI is CC BY-NC 4.0, so these weights are
research / non-commercial
(
NOTICE.md
).
Inference
laya
is the
ConvAI Laya
runtime (
pip install laya==0.3.4
), not Transformers
AutoModel
.
pip install "laya==0.3.4"
export USE_TF=0
import os
import laya
agent = laya.load("Wouze/laya-ara", token=os.environ.get("HF_TOKEN"))
out = agent.predict(
{"message": "الحوالة ما وصلت، أبي استرجاع وإلا بنقلع"},
{
"queue": {
"type": "choice",
"instructions": "Support queue",
"criteria": {
"billing": "payments, refunds",
"technical": "bugs, outages",
"other": "none of the above",
},
},
"refund": {"type": "noul", "instructions": "Asks for a refund?"},
},
)
print(out["answers"])
Local weights:
laya.load("/path/to/artifacts/laya-ar-v48")
. Demo:
examples/predict_triage.py
. This card’s scores:
nlu_benches.json
.
Method
-
Base.
laya-multilingual(Apache-2.0). Englishconvaiinnovations/layais a different checkpoint. - Objective. Official Laya RLCD (policy gradient + soft cross-entropy, group size 4), DDP, fp16.
-
Mix.
MASSIVE-ar train, OSACT4 Subtask A, XNLI-ar cap. Hybrid temperature calibration (
choice:11+floor 3.75). - Protocol. Identical frozen JSONL for stock and fine-tune. Question answering is sentence selection, not span EM/F1. Retrieval transfer is top-1 among k ≤12, not corpus nDCG@10.
Classification
| Task | n | Base | laya-ara | Δ rel. |
|---|---|---|---|---|
| MASSIVE-ar intent (20 options) | 2974 | 0.386 | 0.816 | +111% |
| MASSIVE-ar scenario (18-way) | 2974 | 0.427 | 0.865 | +103% |
| MASSIVE-ar hierarchical intent | 2694 | 0.536 | 0.893 | +67% |
| XNLI-ar | 5010 | 0.686 | 0.723 | +5% |
| OSACT4-A (macro-F1) | 1000 | 0.726 | 0.862 | +19% |
Published stock MASSIVE-ar intent on the Laya harness is about 0.38–0.40. XNLI remains below typical AraBERT fine-tunes (~0.80). Zero-shot AJGT and OSACT-HS decrease relative to stock. TyDiQA-ar sentence selection is 0.359 → 0.413.
Short-list reranking (this card)
No MIRACL train in this mix. Metric: top-1 among ≤12. The dedicated reranker is
laya-ara-rag
.
| Task | n | Base | laya-ara | Δ rel. |
|---|---|---|---|---|
| Mr.TyDi-ar | 2000 | 0.176 | 0.260 | +48% |
| SadeemQuestion | 2089 | 0.168 | 0.242 | +44% |
| MLQA-ar | 2000 | 0.133 | 0.214 | +61% |
| MIRACL-ar (dev) | 2896 | 0.153 | 0.210 | +37% |
All seven listwise files improve. Pairwise Wikipedia/BM25 relevance can still favor stock.
Related models
| Model | Role |
|---|---|
| laya-ara (this) | Arabic intent, NLI, offensive-language A |
laya-ara-rag
|
Arabic short-list relevance / rerank |
Contact
Licensing, evaluation access, or collaboration: Mohammad Alkhenizan on LinkedIn .
Limitations
No token-level NER or span-extraction head. Offensive-language F1 is a research score, not a moderation guarantee. Diglossia is unmeasured beyond MASSIVE (ar-SA MSA) and OSACT tweets. Citations:
citations.bib
.
@misc{alkhenizan2026layaara,
title = {laya-ara: Arabic typed-decision fine-tuning of laya-multilingual},
author = {Alkhenizan, Mohammad},
year = {2026},
howpublished = {\url{https://huggingface.co/Wouze/laya-ara}}
}
Runs of Wouze laya-ara on huggingface.co
182
Total runs
5
24-hour runs
23
3-day runs
176
7-day runs
176
30-day runs
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