aomar85 / StanceNakba-EXP5

A2NLP at StanceNakba 2026: AraBERT-Based Arabic Stance Detection (Subtask B)

This repository contains the official submission of Team A2NLP to the StanceNakba 2026 Shared Task – Subtask B (Topic-Based Stance Detection) , co-located with LREC-COLING 2026 .

Base model: aubmindlab/bert-base-arabertv02-twitter
Best Validation Macro-F1: 0.8434

Team A2NLP

A2NLP is a research team focusing on Arabic Natural Language Processing, with interests in stance detection, political discourse analysis, and transformer-based modeling.

A2NLP-StanceNakba2026-SubtaskB-AraBERTv02-Twitter

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02-twitter on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4374
• Accuracy: 0.8452
• Macro F1: 0.8434
• Weighted F1: 0.8443
• F1 Pro: 0.8870
• F1 Against: 0.8346
• F1 Neutral: 0.8085

Model Description

This model performs Arabic stance classification with three labels:

• pro

• against

• neutral

The architecture is based on BERT-base and fine-tuned using a prompt-based input formulation that explicitly conditions stance prediction on the topic.

Input Format

During training and inference, each instance is formatted as:

الهدف: {topic} [SEP] الموقف من: {sentence}

This prompt-based concatenation strategy was used to explicitly inject the topic context into the transformer encoder.

The model outputs a probability distribution over the three stance labels using a softmax classification head.

Intended Uses & Limitations

Intended Use

• Arabic stance detection in social media text.

• Topic-conditioned stance classification.

• Research and shared-task benchmarking.

The model is particularly suited for:

• Political discourse analysis.

• Arabic Twitter stance modeling.

• Experimental NLP research on stance detection.

Limitations

• The model was trained on Arabic Twitter-style text and may not generalize well to:

  • Formal Arabic prose

  • Long documents

  • Non-political domains

• Sensitive to distribution shift.

• Performance may degrade on unseen topics.

• No external data augmentation was used.

This model should not be used for high-stakes automated decision-making.

Training and Evaluation Data

The model was trained on the official StanceNakba Subtask B train/validation dataset provided by the shared task organizers.

• Language: Arabic

• Domain: Social media (Twitter-style text)

• Labels: pro , against , neutral

No external labeled data were used.

Data Processing

The following preprocessing steps were applied:

• Emoji normalization using emoji.demojize

• Removal of non-Arabic characters

• Removal of URLs, mentions, and hashtags

• Diacritics removal

• Arabic normalization:

  • Alef variants → ا

  • ى → ي

  • ة → ه

  • Removal of tatweel

• Whitespace normalization

• Duplicate sentence removal

• Label encoding:

  {"pro": 0, "against": 1, "neutral": 2}

Training procedure

Cross-Validation Strategy

The model was trained using:

• 5-fold Stratified Cross-Validation

• StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

This repository corresponds to Fold 4 , which achieved the best validation macro-F1 score.

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Loss Function

To address class imbalance, weighted cross-entropy loss was used.

Class weights were computed using:

compute_class_weight(class_weight="balanced")

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 5.0.0
• Pytorch 2.9.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2

Reproducibility

• 5-fold Stratified Cross-Validation
• Seed: 42
• Weighted Cross-Entropy Loss
• Early Stopping (patience=2)
• Best checkpoint selected by Macro-F1