turkish-ner-mBERT-a

This model is a fine-tuned version of bert-base-multilingual-cased on the turkish_ner dataset. It achieves the following results on the evaluation set:

• Loss: 0.3741
• F1: 0.5210
• Precision: 0.5447
• Recall: 0.4992
• Accuracy: 0.8769

Model description

More information needed

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.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: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall	Accuracy
No log	1.0	125	0.3508	0.4950	0.5369	0.4592	0.8669
No log	2.0	250	0.3426	0.5253	0.5890	0.4740	0.8757
No log	3.0	375	0.3746	0.5512	0.5718	0.5321	0.8785
0.2477	4.0	500	0.4057	0.5461	0.5302	0.5629	0.8722
0.2477	5.0	625	0.4334	0.5455	0.5393	0.5518	0.8734

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0