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)

Entity	Precision	Recall	F1	Support
LOC	0.8769	0.9275	0.9015	676
PERS	0.8640	0.8335	0.8485	907
ORG	0.7130	0.7037	0.7083	459
MISC	0.6571	0.5679	0.6093	243
micro	0.8185	0.8070	0.8127	2285

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

python

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.