This model is a fine-tuned version of cahya/NusaBert-v1.3 on the grit-id/id_nergrit_corpus ner dataset.
It supports a context length of 8192, the same as the model cahya/NusaBert-v1.3 which was pre-trained from scratch using ModernBERT architecture.
It achieves the following results on the evaluation set:
Loss: 0.2174
Precision: 0.8010
Recall: 0.8338
F1: 0.8171
Accuracy: 0.9477
Model description
The dataset contains 19 following entities
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'GPE': Geopolitical Entity
'LAW': Law Entity (such as Undang-Undang)
'LOC': Location
'MON': Money
'NOR': Political Organization
'ORD': Ordinal
'ORG': Organization
'PER': Person
'PRC': Percent
'PRD': Product
'QTY': Quantity
'REG': Religion
'TIM': Time
'WOA': Work of Art
'LAN': Language
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 64
total_eval_batch_size: 64
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: 3.0
Training results
Framework versions
Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 2.19.2
Tokenizers 0.21.0
Usage
from transformers import pipeline
ner = pipeline("ner", model="cahya/NusaBert-ner-v1.3", grouped_entities=True)
text = "Jakarta, April 2025 - Polisi mengungkap sosok teman pemberi uang palsu kepada artis Sekar Arum Widara. Sosok tersebut ternyata adalah Bayu Setio Aribowo (BS), pegawai nonaktif Garuda yang ditangkap Polsek Tanah Abang di kasus serupa."
result = ner(text)
print(result)
