This model is a fine-tuned version of dbmdz/distilbert-base-turkish-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1927
F1: 0.8338
Roc Auc: 0.9092
Accuracy: 0.8047

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: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.2899	1.0	1151	0.2053	0.6418	0.7738	0.6719
0.1846	2.0	2302	0.1777	0.7480	0.8434	0.7461
0.1432	3.0	3453	0.1633	0.7879	0.8866	0.7656
0.1241	4.0	4604	0.1508	0.8256	0.9037	0.7891
0.0961	5.0	5755	0.1621	0.8203	0.9048	0.7969
0.065	6.0	6906	0.1733	0.8108	0.9092	0.7969
0.0548	7.0	8057	0.1848	0.8238	0.8993	0.7930
0.0496	8.0	9208	0.1875	0.8130	0.9055	0.7969
0.0413	9.0	10359	0.1905	0.8359	0.9096	0.8086
0.038	10.0	11510	0.1927	0.8338	0.9092	0.8047

Framework versions

Transformers 4.37.0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

alionder/distilbert_turk

distilbert_turk

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alionder/
distilbert_turk