Text Classification
Transformers
Safetensors
Laya
Arabic
modernbert
feature-extraction
arabic
nlu
intent-classification
natural-language-inference
custom_code
Eval Results (legacy)
Instructions to use Wouze/laya-ara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wouze/laya-ara with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wouze/laya-ara", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Wouze/laya-ara", trust_remote_code=True) model = AutoModel.from_pretrained("Wouze/laya-ara", trust_remote_code=True, device_map="auto") - Laya
How to use Wouze/laya-ara with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Update RESULTS.md
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RESULTS.md
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# Results — laya-ara
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Frozen Laya JSONL. Stock is `convaiinnovations/laya-multilingual`. Relative lift is *(model − stock) / stock*.
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Card: [`README.md`](README.md) · JSON: [`results/nlu_benches.json`](results/nlu_benches.json) · all models: [`results/all_cards.json`](results/all_cards.json) · RAG: [`../laya-ara-rag/RESULTS.md`](../laya-ara-rag/RESULTS.md)
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## Highlights vs base
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| Arabic task | *n* | Base | laya-ara | Δ rel. |
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|---|---:|---:|---:|---:|
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| Intent, 20 options (MASSIVE ar-SA) | 2974 | 0.386 | **0.816** | **+111%** |
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| Scenario, 18-way | 2974 | 0.427 | **0.865** | **+103%** |
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| Hierarchical intent | 2694 | 0.536 | **0.893** | **+67%** |
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| OSACT4-A macro-F1 | 1000 | 0.726 | **0.862** | **+19%** |
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| XNLI-ar | 5010 | 0.686 | **0.723** | +5% |
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## Zero-shot NLU (this mix)
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| Task | *n* | Base | laya-ara |
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|---|---:|---:|---:|
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| OSACT4-HS (acc / F1) | 1000 | 0.932 / 0.655 | 0.875 / 0.645 |
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| AJGT | 360 | 0.833 | 0.756 |
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| LABR binary | 2348 | 0.759 | 0.766 |
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| ASTD hold (acc / F1) | 1500 | 0.295 / 0.286 | 0.326 / 0.297 |
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| TyDiQA-ar sentence pick | 298 | 0.359 / 0.155 | 0.413 / 0.170 |
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## Short-list rerank (this card, no MIRACL train)
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| Task | *n* | Base | laya-ara | Δ rel. |
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|---|---:|---:|---:|---:|
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| Mr.TyDi-ar | 2000 | 0.176 | 0.260 | +48% |
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| SadeemQuestion | 2089 | 0.168 | 0.242 | +44% |
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| MLQA-ar | 2000 | 0.133 | 0.214 | +61% |
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| MIRACL-ar (dev) | 2896 | 0.153 | 0.210 | +37% |
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| XPQA-ar | 750 | 0.213 | 0.281 | +32% |
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| PublicHealthQA-ar | 86 | 0.116 | 0.244 | +110% |
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| Mintaka-ar | 2203 | 0.285 | 0.311 | +9% |
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Specialist rerank numbers: [`laya-ara-rag`](https://huggingface.co/Wouze/laya-ara-rag).
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Contact: [Mohammad Alkhenizan](https://www.linkedin.com/in/mohammad-alkhenizan-537623257).
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