Token Classification
Transformers
Safetensors
Arabic
bert
nested-ner
multi-label
arabic
wojood
konooz
knowledgegrapheval-2026
arabicnlp-2026
adaptner
Eval Results (legacy)
Instructions to use NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested") model = AutoModelForTokenClassification.from_pretrained("NAMAA-Space/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
11 days ago
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Align card with camera-ready paper: title, authors, official test results; mark as development-phase checkpoint
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Import from FatimahEmadEldin/KnowledgeGraphEval-2026-AdaptNER-MARBERTv2-Nested
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initial commit
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