Mizan — AraBERT for Arabic Misinformation Detection Fine-tuned aubmindlab/bert-base-arabertv2 for binary classification of Arabic news text as Credible or Fake. This model powers Project Mizan (ميزان), a misinformation detector built to weigh Arabic claims. Model Description Mizan v2 replaces an earlier TF-IDF + KNN pipeline (~65% F1) with a fine-tuned transformer. The shift from bag-of-words to a contextual model is what drove the performance gain — classical methods can't capture meaning or handle Arabic morphology well, while AraBERT is pretrained on large-scale Arabic text.
Base model: aubmindlab/bert-base-arabertv2 Task: Binary sequence classification (Credible / Fake) Language: Arabic Fine-tuned by: Khaled Tarabay
Results Metricv1 (TF-IDF / KNN)v2 (AraBERT)Weighted F1~65%89.3% Evaluated on a held-out 10% test split (5,000 examples, balanced). PrecisionRecallF1Credible0.910.880.89Fake0.880.910.89 Label Map IDLabel0Fake1Credible How to Use pythonfrom transformers import pipeline
classifier = pipeline("text-classification", model="KhaledTTarabay/mizan-arabertv2") result = classifier("مجلس الوزراء يعقد جلسة طارئة لبحث الأزمة الاقتصادية في لبنان") print(result) Or load directly: pythonfrom transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("KhaledTTarabay/mizan-arabertv2") model = AutoModelForSequenceClassification.from_pretrained("KhaledTTarabay/mizan-arabertv2") Training Details
Dataset: AFND (Arabic Fake News Dataset), Khalil et al. (2022), Data in Brief, doi:10.1016/j.dib.2022.108141 Training size: ~50,000 articles, stratified and balanced across the two classes Split: 80% train / 10% validation / 10% test Epochs: 3 Batch size: 16 Learning rate: 2e-5 Max sequence length: 128 Hardware: Single T4 GPU (Google Colab), mixed-precision (fp16)
Intended Use Built to flag potentially misleading Arabic news text and surface a confidence signal for human judgment. Best suited for news-style content similar to its training distribution. Limitations
Arabic only. English is not supported in this version. Trained on AFND alone — may not generalize across all Arabic dialects or domains. Weak supervision. AFND labels can be noisy, which can affect predictions on out-of-distribution text. Not a substitute for human fact-checking. It surfaces a signal, not a verdict.
Citation If you use this model, please cite the AFND dataset:
Khalil, A. et al. (2022). AFND: Arabic Fake News Dataset. Data in Brief. doi:10.1016/j.dib.2022.108141
Part of Project Mizan. Model card drafted with AI assistance.