model_from_berturk_upos_22Jan – AI Model by Buseak | AlphaNeural AI

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model_from_berturk_upos_22Jan

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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model_from_berturk_upos_22Jan

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

Loss: 0.0130
Precision: 0.9961
Recall: 0.9953
F1: 0.9957
Accuracy: 0.9967

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	170	0.2285	0.9100	0.9079	0.9089	0.9352
No log	2.0	340	0.1694	0.9303	0.9316	0.9310	0.9510
0.3728	3.0	510	0.1396	0.9420	0.9420	0.9420	0.9589
0.3728	4.0	680	0.1114	0.9561	0.9547	0.9554	0.9681
0.3728	5.0	850	0.0880	0.9657	0.9653	0.9655	0.9754
0.1318	6.0	1020	0.0670	0.9752	0.9730	0.9741	0.9814
0.1318	7.0	1190	0.0535	0.9801	0.9790	0.9795	0.9850
0.1318	8.0	1360	0.0407	0.9867	0.9842	0.9854	0.9894
0.0764	9.0	1530	0.0312	0.9902	0.9881	0.9892	0.9920
0.0764	10.0	1700	0.0254	0.9919	0.9903	0.9911	0.9935
0.0764	11.0	1870	0.0203	0.9939	0.9925	0.9932	0.9948
0.0466	12.0	2040	0.0169	0.9949	0.9939	0.9944	0.9958
0.0466	13.0	2210	0.0148	0.9955	0.9947	0.9951	0.9961
0.0466	14.0	2380	0.0135	0.9959	0.9949	0.9954	0.9966
0.0329	15.0	2550	0.0130	0.9961	0.9953	0.9957	0.9967

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu116
Datasets 2.8.0
Tokenizers 0.13.2