arabert-ner-anercorp

AraBERT fine-tuned for Arabic NER (ANERCorp)

Fine-tune of aubmindlab/bert-base-arabertv02 for Arabic Named Entity Recognition on ANERCorp. Tags tokens with four entity types using the BIO scheme: PERS, LOC, ORG, MISC.

Results (ANERCorp test split, entity-level / span-level)

F1 is computed at the entity (span) level: a prediction counts as correct only when the full entity span and its type match the gold annotation.

Training

• Base: aubmindlab/bert-base-arabertv02
• 3 epochs, lr 2e-5, batch size 16, weight decay 0.01
• Sub-word alignment: the first sub-token of each word keeps the label; the remaining sub-tokens are set to -100 and ignored by the loss.
• Sentences segmented on . ! ? before tokenization to preserve context.

Usage

from transformers import pipeline
ner = pipeline("token-classification", model="Komail262/arabert-ner-anercorp", aggregation_strategy="simple")
ner("أعلن المدير التنفيذي لشركة أبل تيم كوك عن افتتاح فرع جديد في الرياض.")

License & attribution

This model is a fine-tune of aubmindlab/bert-base-arabertv02 (AraBERT) on the ANERCorp dataset. Rights to the base model and dataset remain with their original authors; please consult their pages for licensing terms.

Limitations

• Trained on ANERCorp (news-domain Modern Standard Arabic); performance on dialectal or social-media Arabic is expected to be lower.
• MISC and ORG lag behind LOC and PERS. MISC is a heterogeneous category with the least training support.
• Not intended for high-stakes use without domain-specific evaluation.