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laya-ara-rag

Wouze / laya-ara-rag

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Total runs: 222
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7-day runs: 60
30-day runs: 222
Model's Last Updated: September 24 2026
text-classification

Introduction of laya-ara-rag

Model Details of laya-ara-rag

laya-ara-rag

Arabic short-list reranker — passage relevance and k ≤12 ranking on laya-multilingual .

laya-ara-rag

Mohammad Alkhenizan · 21 September 2026 · LinkedIn

Hugging Face · GitHub · NLU: laya-ara

Highlights vs laya-multilingual

Relative lift is (fine-tune − stock) / stock . Metric is top-1 among ≤12 candidates, not corpus nDCG@10.

Arabic task n Base laya-ara-rag Relative lift
MIRACL-ar rerank (dev) 2896 0.153 0.588 +284% (3.8×)
Mr.TyDi-ar rerank 2000 0.176 0.632 +259% (3.6×)
MLQA-ar rerank 2000 0.133 0.582 +338% (4.4×)
SadeemQuestion rerank 2089 0.168 0.871 +418% (5.2×)
XPQA-ar rerank 750 0.213 0.665 +212% (3.1×)
In-domain pairwise relevance 112 0.812 0.938 +16%

Mintaka entity ranking does not improve (0.403 predecessor vs 0.388). Full tables: RESULTS.md . This card’s JSON: rag_all_benches.json . All cards: all_cards.json .

Abstract

laya-ara-rag is a fine-tune of convaiinnovations/laya-multilingual (mmBERT-base with a Laya typed-decision head, ~322M) for Arabic pairwise relevance ( noul ) and listwise reranking ( choice ). It is not generative and not a first-stage retriever. Training uses official Laya RLCD on two RTX 3090 GPUs.

The mix continues from a MIRACL-ar Wikipedia specialist, then adds in-house Arabic retrieval logs plus a MIRACL-ar train replay and a small MASSIVE-ar cap. XNLI is withheld. Evaluation is top-1 among a shortlist of k ≤12.

Inference

laya is the ConvAI Laya runtime ( pip install laya==0.3.4 ). 

pip install "laya==0.3.4"
export USE_TF=0

import os
import laya

agent = laya.load("Wouze/laya-ara-rag", token=os.environ.get("HF_TOKEN"))
out = agent.predict(
    {"query": "ما حكم الوضوء قبل قراءة القرآن؟"},
    {
        "passage": {
            "type": "choice",
            "instructions": "Which passage is most relevant to the query?",
            "criteria": {
                "a": "الوضوء شرط للصلاة لا للقراءة عند جمهور الفقهاء.",
                "b": "زكاة الفطر تجب على كل مسلم قبل صلاة العيد.",
                "c": "صيام عاشوراء سنة مؤكدة عند الحنابلة.",
            },
        },
        "relevant": {
            "type": "noul",
            "instructions": "Is passage A relevant to the query?",
        },
    },
)
print(out["answers"])

The example passages are illustrative. Demo: examples/predict_rerank.py . Scores: rag_all_benches.json .

Method
  • Base. laya-multilingual (Apache-2.0). Init from the MIRACL-ar specialist, not from laya-ara.
  • Objective. Official Laya RLCD, DDP, fp16. One epoch, 35,591 items.
  • Public mix. MIRACL-ar train pairwise + listwise; MASSIVE-ar cap 2,000.
  • In-house mix. Arabic retrieval logs (not redistributed): teacher-distilled pairs with a minimum score gap, listwise gold = teacher rank-1, sealed hold-out. See DATA.md .
  • Protocol. Frozen JSONL. Pairwise snippets ~800 characters; listwise ~400 so k = 12 fits max_len 1024.
Public listwise rerank (k ≤ 12)

Chance ≈ 0.08–0.11. MIRACL-ar train is in the mix (dev is in-family). Other rows are transfer.

Task n Kind Base laya-ara laya-ara-rag Δ vs base
MIRACL-ar (dev) 2896 in-family 0.153 0.210 0.588 +284%
Mr.TyDi-ar 2000 transfer 0.176 0.260 0.632 +259%
SadeemQuestion 2089 transfer 0.168 0.242 0.871 +418%
MLQA-ar 2000 transfer 0.133 0.214 0.582 +338%
XPQA-ar 750 transfer 0.213 0.281 0.665 +212%
PublicHealthQA-ar 86 transfer 0.116 0.244 0.488 +321%
Mintaka-ar (entity) 2203 transfer 0.285 0.311 0.388 +36%

Mintaka listwise favors the Wikipedia-only predecessor (0.403). PublicHealthQA n is small.

Public pairwise relevance

One gold + one negative. Chance = 0.50.

Task n Base laya-ara laya-ara-rag
MIRACL-ar (dev) 5792 0.688 0.629 0.788
Mr.TyDi-ar 4000 0.755 0.740 0.834
SadeemQuestion 4178 0.837 0.919 0.941
PublicHealthQA-ar 172 0.791 0.773 0.866
Mintaka-ar 4406 0.537 0.616 0.513
MLQA-ar 4000 0.731 0.784 0.706
XPQA-ar 1500 0.623 0.697 0.615

Listwise transfer does not imply pairwise transfer. Entity and product pairwise still favor the NLU card.

In-domain exam

Sealed hold-out on the in-house Arabic retrieval stack (queries and passages not in this repository).

Exam n Base laya-ara-rag Δ vs base
Human pairwise 112 0.812 0.938 +16%
Teacher pairwise hold 400 0.640 0.792 +24%
Teacher listwise hold 200 0.110 0.575 +423%

Teacher-hold is distillation agreement, not clicks. The 21-query human listwise set is too small to headline.

Related models
Model Role
laya-ara Arabic intent, NLI, OSACT-A
laya-ara-rag (this) Arabic short-list relevance / rerank
Contact

Licensing, evaluation access, or collaboration: Mohammad Alkhenizan on LinkedIn .

Limitations

Not a first-stage retriever and not official MIRACL nDCG@10. No span extraction or token NER. Mintaka / MLQA / XPQA pairwise can drop versus laya-ara. 

@misc{alkhenizan2026layaararag,
  title        = {laya-ara-rag: Arabic short-list reranking with laya-multilingual},
  author       = {Alkhenizan, Mohammad},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/Wouze/laya-ara-rag}}
}

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Total runs: 194
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Updated:September 24 2026