from transformers import pipeline

model_id = "yeniguno/bert-turkish-organization-ner"

ner = pipeline("ner", model=model_id, aggregation_strategy="simple")

text = "Microsoft ve Koç Holding birlikte bir proje başlattı."

print(ner(text))
"""
[{'entity_group': 'ORG', 'score': np.float32(0.99849355), 'word': 'microsoft', 'start': 0, 'end': 9},
{'entity_group': 'ORG', 'score': np.float32(0.9970416), 'word': 'koc holding', 'start': 13, 'end': 24}]
"""

Intended uses & limitations

Guardrails in LLM applications: detect and flag organization names in user prompts or model outputs.
Content filtering & compliance: e.g. anonymization, redaction, or entity-specific monitoring.
Analytics: extracting organization mentions from Turkish text for search, clustering, or knowledge graphs.

Training and evaluation data

It achieves the following results on the evaluation set:

Loss: 0.1152
F1: 0.9159

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.0617	1.0	8080	0.0679	0.8990
0.0471	2.0	16160	0.0640	0.9105
0.0295	3.0	24240	0.0846	0.9110
0.0277	4.0	32320	0.0959	0.9153
0.0116	5.0	40400	0.1152	0.9159

Framework versions

Transformers 4.55.2
Pytorch 2.9.0.dev20250816+cu128
Datasets 4.0.0
Tokenizers 0.21.4

yeniguno/bert-turkish-organization-ner-uncased

turkish-organization-ner-dataset

Model description

How to use it

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yeniguno/
bert-turkish-organization-ner-uncased