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
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Download RESULTS.md from Wouze/laya-ara: direct link, hf CLI and curl.
- Browser
- Download file 1.86 kB
-
https://huggingface.co/Wouze/laya-ara/resolve/main/RESULTS.md
- Command line
-
hf download hf://Wouze/laya-ara/RESULTS.md
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curl -L -o RESULTS.md https://huggingface.co/Wouze/laya-ara/resolve/main/RESULTS.md
1.86 kB
Results — laya-ara
Frozen Laya JSONL. Stock is convaiinnovations/laya-multilingual. Relative lift is (model − stock) / stock.
Card: README.md · JSON: nlu_benches.json · all models: all_cards.json · RAG: laya-ara-rag/RESULTS.md
Highlights vs base
| Arabic task | n | Base | laya-ara | Δ rel. |
|---|---|---|---|---|
| Intent, 20 options (MASSIVE ar-SA) | 2974 | 0.386 | 0.816 | +111% |
| Scenario, 18-way | 2974 | 0.427 | 0.865 | +103% |
| Hierarchical intent | 2694 | 0.536 | 0.893 | +67% |
| OSACT4-A macro-F1 | 1000 | 0.726 | 0.862 | +19% |
| XNLI-ar | 5010 | 0.686 | 0.723 | +5% |
Zero-shot NLU (this mix)
| Task | n | Base | laya-ara |
|---|---|---|---|
| OSACT4-HS (acc / F1) | 1000 | 0.932 / 0.655 | 0.875 / 0.645 |
| AJGT | 360 | 0.833 | 0.756 |
| LABR binary | 2348 | 0.759 | 0.766 |
| ASTD hold (acc / F1) | 1500 | 0.295 / 0.286 | 0.326 / 0.297 |
| TyDiQA-ar sentence pick | 298 | 0.359 / 0.155 | 0.413 / 0.170 |
Short-list rerank (this card, no MIRACL train)
| 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% |
| XPQA-ar | 750 | 0.213 | 0.281 | +32% |
| PublicHealthQA-ar | 86 | 0.116 | 0.244 | +110% |
| Mintaka-ar | 2203 | 0.285 | 0.311 | +9% |
Specialist rerank numbers: laya-ara-rag.
Contact: Mohammad Alkhenizan.