bert-base-turkish-cased_allnli_tr

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: 0.5771
Accuracy: 0.7978

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8559	0.03	1000	0.7577	0.6798
0.6612	0.07	2000	0.7263	0.6958
0.6115	0.1	3000	0.6431	0.7364
0.5916	0.14	4000	0.6347	0.7407
0.5719	0.17	5000	0.6317	0.7483
0.5575	0.2	6000	0.6034	0.7544
0.5521	0.24	7000	0.6148	0.7568
0.5393	0.27	8000	0.5931	0.7610
0.5382	0.31	9000	0.5866	0.7665
0.5306	0.34	10000	0.5881	0.7594
0.5295	0.37	11000	0.6120	0.7632
0.5225	0.41	12000	0.5620	0.7759
0.5112	0.44	13000	0.5641	0.7769
0.5133	0.48	14000	0.5571	0.7798
0.5023	0.51	15000	0.5719	0.7722
0.5017	0.54	16000	0.5482	0.7844
0.5111	0.58	17000	0.5503	0.7800
0.4929	0.61	18000	0.5502	0.7836
0.4923	0.65	19000	0.5424	0.7843
0.4894	0.68	20000	0.5417	0.7851
0.4877	0.71	21000	0.5514	0.7841
0.4818	0.75	22000	0.5494	0.7848
0.4898	0.78	23000	0.5450	0.7859
0.4823	0.82	24000	0.5417	0.7878
0.4806	0.85	25000	0.5354	0.7875
0.4779	0.88	26000	0.5338	0.7848
0.4744	0.92	27000	0.5277	0.7934
0.4678	0.95	28000	0.5507	0.7871
0.4727	0.99	29000	0.5603	0.7789
0.4243	1.02	30000	0.5626	0.7894
0.3955	1.05	31000	0.5324	0.7939
0.4022	1.09	32000	0.5322	0.7925
0.3976	1.12	33000	0.5450	0.7920
0.3913	1.15	34000	0.5464	0.7948
0.406	1.19	35000	0.5406	0.7958
0.3875	1.22	36000	0.5489	0.7878
0.4024	1.26	37000	0.5427	0.7925
0.3988	1.29	38000	0.5335	0.7904
0.393	1.32	39000	0.5415	0.7923
0.3988	1.36	40000	0.5385	0.7962
0.3912	1.39	41000	0.5383	0.7950
0.3949	1.43	42000	0.5415	0.7931
0.3902	1.46	43000	0.5438	0.7893
0.3948	1.49	44000	0.5348	0.7906
0.3921	1.53	45000	0.5361	0.7890
0.3944	1.56	46000	0.5419	0.7953
0.3959	1.6	47000	0.5402	0.7967
0.3926	1.63	48000	0.5429	0.7925
0.3854	1.66	49000	0.5346	0.7959
0.3864	1.7	50000	0.5241	0.7979
0.385	1.73	51000	0.5149	0.8002
0.3871	1.77	52000	0.5325	0.8002
0.3819	1.8	53000	0.5332	0.8022
0.384	1.83	54000	0.5419	0.7873
0.3899	1.87	55000	0.5225	0.7974
0.3894	1.9	56000	0.5358	0.7977
0.3838	1.94	57000	0.5264	0.7988
0.3881	1.97	58000	0.5280	0.7956
0.3756	2.0	59000	0.5601	0.7969
0.3156	2.04	60000	0.5936	0.7925
0.3125	2.07	61000	0.5898	0.7938
0.3179	2.11	62000	0.5591	0.7981
0.315	2.14	63000	0.5853	0.7970
0.3122	2.17	64000	0.5802	0.7979
0.3105	2.21	65000	0.5758	0.7979
0.3076	2.24	66000	0.5685	0.7980
0.3117	2.28	67000	0.5799	0.7944
0.3108	2.31	68000	0.5742	0.7988
0.3047	2.34	69000	0.5907	0.7921
0.3114	2.38	70000	0.5723	0.7937
0.3035	2.41	71000	0.5944	0.7955
0.3129	2.45	72000	0.5838	0.7928
0.3071	2.48	73000	0.5929	0.7949
0.3061	2.51	74000	0.5794	0.7967
0.3068	2.55	75000	0.5892	0.7954
0.3053	2.58	76000	0.5796	0.7962
0.3117	2.62	77000	0.5763	0.7981
0.3062	2.65	78000	0.5852	0.7964
0.3004	2.68	79000	0.5793	0.7966
0.3146	2.72	80000	0.5693	0.7985
0.3146	2.75	81000	0.5788	0.7982
0.3079	2.79	82000	0.5726	0.7978
0.3058	2.82	83000	0.5677	0.7988
0.3055	2.85	84000	0.5701	0.7982
0.3049	2.89	85000	0.5809	0.7970
0.3044	2.92	86000	0.5741	0.7986
0.3057	2.96	87000	0.5743	0.7980
0.3081	2.99	88000	0.5771	0.7978

Framework versions

Transformers 4.12.3
Pytorch 1.10.0+cu102
Datasets 1.15.1
Tokenizers 0.10.3

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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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8559	0.03	1000	0.7577	0.6798
0.6612	0.07	2000	0.7263	0.6958
0.6115	0.1	3000	0.6431	0.7364
0.5916	0.14	4000	0.6347	0.7407
0.5719	0.17	5000	0.6317	0.7483
0.5575	0.2	6000	0.6034	0.7544
0.5521	0.24	7000	0.6148	0.7568
0.5393	0.27	8000	0.5931	0.7610
0.5382	0.31	9000	0.5866	0.7665
0.5306	0.34	10000	0.5881	0.7594
0.5295	0.37	11000	0.6120	0.7632
0.5225	0.41	12000	0.5620	0.7759
0.5112	0.44	13000	0.5641	0.7769
0.5133	0.48	14000	0.5571	0.7798
0.5023	0.51	15000	0.5719	0.7722
0.5017	0.54	16000	0.5482	0.7844
0.5111	0.58	17000	0.5503	0.7800
0.4929	0.61	18000	0.5502	0.7836
0.4923	0.65	19000	0.5424	0.7843
0.4894	0.68	20000	0.5417	0.7851
0.4877	0.71	21000	0.5514	0.7841
0.4818	0.75	22000	0.5494	0.7848
0.4898	0.78	23000	0.5450	0.7859
0.4823	0.82	24000	0.5417	0.7878
0.4806	0.85	25000	0.5354	0.7875
0.4779	0.88	26000	0.5338	0.7848
0.4744	0.92	27000	0.5277	0.7934
0.4678	0.95	28000	0.5507	0.7871
0.4727	0.99	29000	0.5603	0.7789
0.4243	1.02	30000	0.5626	0.7894
0.3955	1.05	31000	0.5324	0.7939
0.4022	1.09	32000	0.5322	0.7925
0.3976	1.12	33000	0.5450	0.7920
0.3913	1.15	34000	0.5464	0.7948
0.406	1.19	35000	0.5406	0.7958
0.3875	1.22	36000	0.5489	0.7878
0.4024	1.26	37000	0.5427	0.7925
0.3988	1.29	38000	0.5335	0.7904
0.393	1.32	39000	0.5415	0.7923
0.3988	1.36	40000	0.5385	0.7962
0.3912	1.39	41000	0.5383	0.7950
0.3949	1.43	42000	0.5415	0.7931
0.3902	1.46	43000	0.5438	0.7893
0.3948	1.49	44000	0.5348	0.7906
0.3921	1.53	45000	0.5361	0.7890
0.3944	1.56	46000	0.5419	0.7953
0.3959	1.6	47000	0.5402	0.7967
0.3926	1.63	48000	0.5429	0.7925
0.3854	1.66	49000	0.5346	0.7959
0.3864	1.7	50000	0.5241	0.7979
0.385	1.73	51000	0.5149	0.8002
0.3871	1.77	52000	0.5325	0.8002
0.3819	1.8	53000	0.5332	0.8022
0.384	1.83	54000	0.5419	0.7873
0.3899	1.87	55000	0.5225	0.7974
0.3894	1.9	56000	0.5358	0.7977
0.3838	1.94	57000	0.5264	0.7988
0.3881	1.97	58000	0.5280	0.7956
0.3756	2.0	59000	0.5601	0.7969
0.3156	2.04	60000	0.5936	0.7925
0.3125	2.07	61000	0.5898	0.7938
0.3179	2.11	62000	0.5591	0.7981
0.315	2.14	63000	0.5853	0.7970
0.3122	2.17	64000	0.5802	0.7979
0.3105	2.21	65000	0.5758	0.7979
0.3076	2.24	66000	0.5685	0.7980
0.3117	2.28	67000	0.5799	0.7944
0.3108	2.31	68000	0.5742	0.7988
0.3047	2.34	69000	0.5907	0.7921
0.3114	2.38	70000	0.5723	0.7937
0.3035	2.41	71000	0.5944	0.7955
0.3129	2.45	72000	0.5838	0.7928
0.3071	2.48	73000	0.5929	0.7949
0.3061	2.51	74000	0.5794	0.7967
0.3068	2.55	75000	0.5892	0.7954
0.3053	2.58	76000	0.5796	0.7962
0.3117	2.62	77000	0.5763	0.7981
0.3062	2.65	78000	0.5852	0.7964
0.3004	2.68	79000	0.5793	0.7966
0.3146	2.72	80000	0.5693	0.7985
0.3146	2.75	81000	0.5788	0.7982
0.3079	2.79	82000	0.5726	0.7978
0.3058	2.82	83000	0.5677	0.7988
0.3055	2.85	84000	0.5701	0.7982
0.3049	2.89	85000	0.5809	0.7970
0.3044	2.92	86000	0.5741	0.7986
0.3057	2.96	87000	0.5743	0.7980
0.3081	2.99	88000	0.5771	0.7978

Framework versions

Transformers 4.12.3

Pytorch 1.10.0+cu102

Datasets 1.15.1

Tokenizers 0.10.3

emrecan/bert-base-turkish-cased-allnli_tr

bert-base-turkish-cased_allnli_tr

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bert-base-turkish-cased_allnli_tr

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions