Model description

Base Model: CAMeL-Lab/bert-base-arabic-camelbert-mix
Task: Named Entity Recognition (NER)
Language: Arabic
Training Data: WikiAnn dataset for Arabic

Training procedure

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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1892	1.0	1250	0.2003	0.8653	0.8677	0.8665	0.9430
0.123	2.0	2500	0.1912	0.8802	0.8826	0.8814	0.9493
0.0809	3.0	3750	0.1942	0.8928	0.8969	0.8948	0.9539

Usage

python

from transformers import pipeline

# Load the NER pipeline
nlp = pipeline("ner", model="Tevfik-istanbullu/camelbert-ner-arabic")

# Example text
text = "يعمل محمد في شركة جوجل في دبي"
results = nlp(text)
print(results)

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3