turkish-hs-2class-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: 0.5990
• Accuracy: 0.8748
• Macro F1: 0.8696

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1
0.585	0.1462	100	0.4524	0.7797	0.7752
0.4357	0.2924	200	0.3881	0.8099	0.8065
0.4058	0.4386	300	0.3497	0.8428	0.8337
0.3747	0.5848	400	0.3356	0.8583	0.8504
0.3571	0.7310	500	0.3340	0.8355	0.8318
0.3311	0.8772	600	0.3091	0.8665	0.8603
0.3065	1.0234	700	0.3085	0.8693	0.8614
0.3151	1.1696	800	0.2962	0.8684	0.8616
0.2795	1.3158	900	0.2885	0.8784	0.8722
0.263	1.4620	1000	0.3155	0.8647	0.8610
0.3116	1.6082	1100	0.3208	0.8629	0.8580
0.2744	1.7544	1200	0.3005	0.8803	0.8746
0.2868	1.9006	1300	0.3130	0.8675	0.8631
0.2454	2.0468	1400	0.3330	0.8611	0.8575
0.2262	2.1930	1500	0.3454	0.8629	0.8587
0.2483	2.3392	1600	0.3144	0.8757	0.8712
0.2451	2.4854	1700	0.3237	0.8647	0.8607
0.2384	2.6316	1800	0.3390	0.8638	0.8605
0.2347	2.7778	1900	0.3635	0.8611	0.8580
0.2683	2.9240	2000	0.3083	0.8748	0.8679
0.2047	3.0702	2100	0.3251	0.8748	0.8696
0.21	3.2164	2200	0.3381	0.8839	0.8784
0.1948	3.3626	2300	0.3383	0.8803	0.8754
0.1953	3.5088	2400	0.3495	0.8757	0.8707
0.1873	3.6550	2500	0.3539	0.8857	0.8787
0.201	3.8012	2600	0.3520	0.8784	0.8731
0.1935	3.9474	2700	0.3656	0.8629	0.8588
0.189	4.0936	2800	0.3486	0.8793	0.8737
0.1312	4.2398	2900	0.3845	0.8793	0.8737
0.1824	4.3860	3000	0.4035	0.8757	0.8696
0.1994	4.5322	3100	0.3820	0.8784	0.8737
0.1535	4.6784	3200	0.4042	0.8739	0.8683
0.1902	4.8246	3300	0.3990	0.8803	0.8730
0.1622	4.9708	3400	0.4224	0.8665	0.8619
0.1319	5.1170	3500	0.4311	0.8748	0.8694
0.1533	5.2632	3600	0.4505	0.8647	0.8609
0.1251	5.4094	3700	0.4523	0.8720	0.8670
0.1473	5.5556	3800	0.4535	0.8812	0.8762
0.1439	5.7018	3900	0.4566	0.8784	0.8727
0.1487	5.8480	4000	0.4472	0.8830	0.8772
0.1539	5.9942	4100	0.4414	0.8803	0.8743
0.1212	6.1404	4200	0.4778	0.8766	0.8720
0.1187	6.2865	4300	0.4734	0.8857	0.8806
0.1007	6.4327	4400	0.5087	0.8784	0.8731
0.1263	6.5789	4500	0.4983	0.8876	0.8826
0.1264	6.7251	4600	0.4999	0.8784	0.8737
0.1257	6.8713	4700	0.4943	0.8839	0.8776
0.121	7.0175	4800	0.5123	0.8775	0.8708
0.1012	7.1637	4900	0.5321	0.8775	0.8724
0.13	7.3099	5000	0.5426	0.8748	0.8703
0.1061	7.4561	5100	0.5380	0.8784	0.8732
0.1028	7.6023	5200	0.5481	0.8739	0.8685
0.1236	7.7485	5300	0.5456	0.8803	0.8740
0.0889	7.8947	5400	0.5653	0.8784	0.8722
0.0912	8.0409	5500	0.5781	0.8748	0.8699
0.1035	8.1871	5600	0.5711	0.8793	0.8736
0.109	8.3333	5700	0.5692	0.8793	0.8729
0.0996	8.4795	5800	0.5694	0.8793	0.8736
0.1158	8.6257	5900	0.5886	0.8720	0.8670
0.1008	8.7719	6000	0.5973	0.8702	0.8660
0.0859	8.9181	6100	0.5815	0.8803	0.8746
0.0927	9.0643	6200	0.5840	0.8766	0.8711
0.0918	9.2105	6300	0.5862	0.8775	0.8724
0.0661	9.3567	6400	0.5912	0.8784	0.8732
0.0884	9.5029	6500	0.5923	0.8784	0.8730
0.0845	9.6491	6600	0.6021	0.8748	0.8698
0.1071	9.7953	6700	0.6044	0.8748	0.8698
0.1016	9.9415	6800	0.5990	0.8748	0.8696

Framework versions

• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0