SanadAI-AraModernBERT-IslamicNER

SanadAI-AraModernBERT-IslamicNER

Arabic Named Entity Recognition model for IslamicEval 2026 Task 1: Span Detection.

This model performs token-level span detection for Islamic texts, identifying important semantic spans including Quranic verses, Hadith components, and citation sources.

The model is based on AraModernBERT and fine-tuned for Arabic Islamic text understanding.

Model Details

• Base Model: NAMAA-Space/AraModernBert-Base-V1.0
• Architecture: ModernBERT for Token Classification
• Task: Span Detection / Token Classification
• Language: Arabic
• Training Dataset: AbirKorched9/IslamicEval2026-Task1-augmented
• Training Epochs: 15
• Framework: PyTorch + Hugging Face Transformers

Labels

The model predicts the following entity spans:

Dataset Composition

The training dataset combines:

The dataset includes automatically generated Quran and Hadith examples together with manually selected hard negatives from Shamela books to improve robustness against false Hadith detection.

Training

The model was fine-tuned using:

• Hugging Face Transformers
• Custom weighted loss training
• AraModernBERT encoder
• Token-level classification objective
• Class weighting to handle entity imbalance

Training configuration:

• Epochs: 15
• Maximum sequence length: 1024
• Mixed precision training: FP16
• Best checkpoint selected using development evaluation metrics

Training Code

The complete training, inference, and post-processing pipeline used to train this model is publicly available:

Repository: https://github.com/AbirKorched/SanadAI-IslamicEval2026/tree/main/task1_span_detection

The implementation includes:

• Dataset preparation and parsing
• Arabic span annotation processing
• AraModernBERT fine-tuning pipeline
• Weighted loss training for imbalanced span labels
• Inference pipeline
• Span-level post-processing and submission generation

The released code allows reproducing the training and inference procedure used for the IslamicEval 2026 Task 1 submission.

Evaluation

Evaluation was performed on the official IslamicEval 2026 Task 1 split.

Intended Use

This model can be used for:

• Arabic Islamic text processing
• Hadith structure extraction
• Quran verse identification
• Retrieval-augmented generation preprocessing
• Arabic NLP research
• Information extraction pipelines