turkish-hs-degree-prediction

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

• Loss: 2.4057
• Mse: 2.4057
• Mae: 1.0103
• R2: 0.6519
• Accuracy: 0.4909

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: 5e-06
• train_batch_size: 16
• eval_batch_size: 20
• 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	Mse	Mae	R2	Accuracy
6.2323	0.1376	100	5.6129	5.6129	1.8340	0.1878	0.1210
5.1946	0.2751	200	4.2740	4.2740	1.5758	0.3815	0.2750
4.1391	0.4127	300	3.6789	3.6789	1.4001	0.4676	0.3203
3.643	0.5502	400	3.4047	3.4047	1.3039	0.5073	0.3664
3.1216	0.6878	500	3.2868	3.2868	1.2569	0.5244	0.3890
3.0051	0.8253	600	3.1198	3.1198	1.2476	0.5486	0.3812
2.7453	0.9629	700	2.9603	2.9603	1.1832	0.5716	0.4082
2.6567	1.1004	800	2.8558	2.8558	1.1548	0.5868	0.4291
2.6505	1.2380	900	2.8028	2.8028	1.1569	0.5944	0.4091
2.4613	1.3755	1000	2.6528	2.6528	1.1165	0.6161	0.4308
2.494	1.5131	1100	2.6037	2.6037	1.1299	0.6232	0.4117
2.5598	1.6506	1200	2.5742	2.5742	1.0774	0.6275	0.4552
2.3135	1.7882	1300	2.5063	2.5063	1.1045	0.6373	0.4169
2.3415	1.9257	1400	2.6034	2.6034	1.0952	0.6233	0.4482
2.1288	2.0633	1500	2.5379	2.5379	1.0790	0.6328	0.4456
2.1298	2.2008	1600	2.5946	2.5946	1.1048	0.6245	0.4360
2.0684	2.3384	1700	2.5250	2.5250	1.0503	0.6346	0.4656
1.9188	2.4759	1800	2.4779	2.4779	1.0466	0.6414	0.4630
2.0123	2.6135	1900	2.4917	2.4917	1.0547	0.6394	0.4639
1.9938	2.7510	2000	2.3888	2.3888	1.0420	0.6543	0.4578
1.8904	2.8886	2100	2.3776	2.3776	1.0356	0.6559	0.4569
1.9122	3.0261	2200	2.4560	2.4560	1.0394	0.6446	0.4630
1.7057	3.1637	2300	2.3577	2.3577	1.0316	0.6588	0.4621
1.7284	3.3012	2400	2.4423	2.4423	1.0436	0.6466	0.4595
1.7337	3.4388	2500	2.4151	2.4151	1.0406	0.6505	0.4682
1.6713	3.5763	2600	2.3681	2.3681	1.0152	0.6573	0.4665
1.8363	3.7139	2700	2.3612	2.3612	1.0313	0.6583	0.4621
1.719	3.8514	2800	2.4504	2.4504	1.0522	0.6454	0.4639
1.5755	3.9890	2900	2.3961	2.3961	1.0258	0.6533	0.4648
1.4174	4.1265	3000	2.4370	2.4370	1.0279	0.6473	0.4822
1.6906	4.2641	3100	2.4672	2.4672	1.0472	0.6430	0.4743
1.4928	4.4017	3200	2.5710	2.5710	1.0684	0.6280	0.4761
1.4598	4.5392	3300	2.4387	2.4387	1.0303	0.6471	0.4813
1.45	4.6768	3400	2.4174	2.4174	1.0193	0.6502	0.4856
1.3921	4.8143	3500	2.3611	2.3611	1.0084	0.6583	0.4822
1.4918	4.9519	3600	2.3255	2.3255	1.0267	0.6635	0.4621
1.3672	5.0894	3700	2.4369	2.4369	1.0370	0.6474	0.4787
1.2711	5.2270	3800	2.4173	2.4173	1.0317	0.6502	0.4752
1.2531	5.3645	3900	2.4008	2.4008	1.0170	0.6526	0.4900
1.3035	5.5021	4000	2.4040	2.4040	1.0133	0.6521	0.4891
1.3362	5.6396	4100	2.3974	2.3974	1.0042	0.6531	0.4943
1.2697	5.7772	4200	2.3708	2.3708	1.0200	0.6569	0.4708
1.2656	5.9147	4300	2.4463	2.4463	1.0346	0.6460	0.4813
1.2401	6.0523	4400	2.4412	2.4412	1.0320	0.6467	0.4778
1.1675	6.1898	4500	2.4380	2.4380	1.0226	0.6472	0.4874
1.3219	6.3274	4600	2.3325	2.3325	0.9982	0.6625	0.4943
1.1802	6.4649	4700	2.3582	2.3582	1.0133	0.6588	0.4778
1.2132	6.6025	4800	2.3610	2.3610	1.0035	0.6584	0.5013
1.109	6.7400	4900	2.4004	2.4004	1.0214	0.6526	0.4735
1.0964	6.8776	5000	2.4150	2.4150	1.0181	0.6505	0.4909
1.1356	7.0151	5100	2.4174	2.4174	1.0027	0.6502	0.5022
1.0781	7.1527	5200	2.4172	2.4172	1.0113	0.6502	0.4917
1.0545	7.2902	5300	2.4086	2.4086	1.0105	0.6515	0.4987
1.063	7.4278	5400	2.4091	2.4091	1.0082	0.6514	0.4874
1.2053	7.5653	5500	2.3560	2.3560	0.9998	0.6591	0.4970
1.0971	7.7029	5600	2.4048	2.4048	1.0113	0.6520	0.4839
1.0788	7.8404	5700	2.4743	2.4743	1.0383	0.6420	0.4648
1.0963	7.9780	5800	2.3513	2.3513	0.9999	0.6598	0.4961
1.0488	8.1155	5900	2.3650	2.3650	1.0014	0.6578	0.4978
1.013	8.2531	6000	2.4043	2.4043	1.0112	0.6521	0.4935
0.9864	8.3906	6100	2.4053	2.4053	1.0108	0.6519	0.4917
1.1504	8.5282	6200	2.4569	2.4569	1.0305	0.6445	0.4735
0.9722	8.6657	6300	2.4546	2.4546	1.0223	0.6448	0.4900
0.9892	8.8033	6400	2.3933	2.3933	1.0060	0.6537	0.4874
0.9928	8.9409	6500	2.4005	2.4005	1.0114	0.6526	0.4874
0.905	9.0784	6600	2.4132	2.4132	1.0160	0.6508	0.4830
0.9691	9.2160	6700	2.3942	2.3942	1.0092	0.6535	0.4961
0.9424	9.3535	6800	2.4073	2.4073	1.0117	0.6516	0.4900
0.8763	9.4911	6900	2.3933	2.3933	1.0009	0.6537	0.5030
1.0059	9.6286	7000	2.3984	2.3984	1.0075	0.6529	0.4952
1.0035	9.7662	7100	2.4082	2.4082	1.0112	0.6515	0.4917
1.1103	9.9037	7200	2.4057	2.4057	1.0103	0.6519	0.4909

Framework versions

• Transformers 4.45.2
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.20.3