pos_tagger_3112_v3

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: 0.7728
• Precision: 0.8922
• Recall: 0.8955
• F1: 0.8938
• Accuracy: 0.9244

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	244	0.3040	0.8905	0.8924	0.8915	0.9215
No log	2.0	488	0.2915	0.8981	0.9006	0.8994	0.9279
0.3896	3.0	732	0.3109	0.8933	0.8932	0.8933	0.9234
0.3896	4.0	976	0.3004	0.8954	0.8983	0.8969	0.9263
0.159	5.0	1220	0.3338	0.8929	0.8946	0.8937	0.9242
0.159	6.0	1464	0.3419	0.8914	0.8958	0.8936	0.9240
0.1038	7.0	1708	0.3840	0.8892	0.8930	0.8911	0.9223
0.1038	8.0	1952	0.3923	0.8857	0.8930	0.8894	0.9213
0.0629	9.0	2196	0.4441	0.8888	0.8914	0.8901	0.9213
0.0629	10.0	2440	0.4769	0.8886	0.8929	0.8908	0.9231
0.0357	11.0	2684	0.4846	0.8859	0.8913	0.8886	0.9199
0.0357	12.0	2928	0.5256	0.8877	0.8895	0.8886	0.9211
0.0212	13.0	3172	0.5554	0.8896	0.8900	0.8898	0.9219
0.0212	14.0	3416	0.5748	0.8870	0.8911	0.8890	0.9207
0.0143	15.0	3660	0.5988	0.8877	0.8916	0.8896	0.9220
0.0143	16.0	3904	0.6047	0.8874	0.8903	0.8888	0.9209
0.0098	17.0	4148	0.6161	0.8846	0.8914	0.8880	0.9199
0.0098	18.0	4392	0.6158	0.8883	0.8929	0.8906	0.9217
0.0072	19.0	4636	0.6216	0.8858	0.8928	0.8893	0.9209
0.0072	20.0	4880	0.6497	0.8892	0.8926	0.8909	0.9215
0.0058	21.0	5124	0.6698	0.8887	0.8919	0.8903	0.9216
0.0058	22.0	5368	0.6582	0.8858	0.8916	0.8887	0.9208
0.0046	23.0	5612	0.6915	0.8866	0.8925	0.8896	0.9212
0.0046	24.0	5856	0.6725	0.8898	0.8928	0.8913	0.9222
0.004	25.0	6100	0.6678	0.8912	0.8961	0.8936	0.9238
0.004	26.0	6344	0.6899	0.8891	0.8933	0.8912	0.9224
0.0034	27.0	6588	0.7082	0.8890	0.8922	0.8906	0.9215
0.0034	28.0	6832	0.7066	0.8903	0.8920	0.8911	0.9228
0.0026	29.0	7076	0.7243	0.8882	0.8938	0.8910	0.9228
0.0026	30.0	7320	0.7322	0.8891	0.8923	0.8907	0.9226
0.0023	31.0	7564	0.7292	0.8909	0.8930	0.8920	0.9230
0.0023	32.0	7808	0.7227	0.8922	0.8947	0.8934	0.9244
0.0027	33.0	8052	0.7231	0.8885	0.8922	0.8903	0.9222
0.0027	34.0	8296	0.7236	0.8907	0.8936	0.8922	0.9233
0.0019	35.0	8540	0.7313	0.8875	0.8895	0.8885	0.9214
0.0019	36.0	8784	0.7240	0.8902	0.8935	0.8919	0.9234
0.0017	37.0	9028	0.7364	0.8903	0.8939	0.8921	0.9233
0.0017	38.0	9272	0.7479	0.8896	0.8929	0.8913	0.9232
0.0013	39.0	9516	0.7511	0.8895	0.8937	0.8916	0.9230
0.0013	40.0	9760	0.7689	0.8896	0.8948	0.8922	0.9234
0.001	41.0	10004	0.7597	0.8909	0.8958	0.8933	0.9238
0.001	42.0	10248	0.7581	0.8897	0.8929	0.8913	0.9230
0.001	43.0	10492	0.7512	0.8919	0.8952	0.8935	0.9244
0.0012	44.0	10736	0.7622	0.8921	0.8957	0.8939	0.9244
0.0012	45.0	10980	0.7707	0.8907	0.8952	0.8930	0.9237
0.001	46.0	11224	0.7700	0.8922	0.8963	0.8942	0.9244
0.001	47.0	11468	0.7742	0.8895	0.8938	0.8916	0.9231
0.0009	48.0	11712	0.7753	0.8911	0.8945	0.8928	0.9239
0.0009	49.0	11956	0.7746	0.8909	0.8944	0.8927	0.9236
0.0008	50.0	12200	0.7728	0.8922	0.8955	0.8938	0.9244

Framework versions

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