pos_tagger_3112

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: 0.5781
• Precision: 0.8885
• Recall: 0.8926
• F1: 0.8906
• Accuracy: 0.9222

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	244	0.2966	0.8889	0.8935	0.8912	0.9215
No log	2.0	488	0.2884	0.8958	0.8969	0.8963	0.9258
0.3778	3.0	732	0.3138	0.8919	0.8913	0.8916	0.9224
0.3778	4.0	976	0.3086	0.8935	0.8960	0.8948	0.9252
0.1601	5.0	1220	0.3354	0.8897	0.8940	0.8919	0.9232
0.1601	6.0	1464	0.3486	0.8863	0.8911	0.8887	0.9212
0.1077	7.0	1708	0.3700	0.8899	0.8945	0.8922	0.9236
0.1077	8.0	1952	0.3920	0.8846	0.8905	0.8875	0.9207
0.0709	9.0	2196	0.4220	0.8860	0.8911	0.8885	0.9211
0.0709	10.0	2440	0.4467	0.8889	0.8934	0.8912	0.9226
0.0447	11.0	2684	0.4730	0.8834	0.8891	0.8862	0.9183
0.0447	12.0	2928	0.5008	0.8869	0.8921	0.8895	0.9217
0.0289	13.0	3172	0.5190	0.8866	0.8919	0.8892	0.9213
0.0289	14.0	3416	0.5384	0.8865	0.8904	0.8884	0.9204
0.0204	15.0	3660	0.5499	0.8852	0.8907	0.8879	0.9205
0.0204	16.0	3904	0.5564	0.8864	0.8923	0.8893	0.9213
0.015	17.0	4148	0.5688	0.8883	0.8920	0.8901	0.9220
0.015	18.0	4392	0.5740	0.8874	0.8927	0.8901	0.9213
0.0116	19.0	4636	0.5742	0.8879	0.8925	0.8902	0.9219
0.0116	20.0	4880	0.5781	0.8885	0.8926	0.8906	0.9222

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0