turkish-hs-4class-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: 0.6299
• Accuracy: 0.8386
• Macro F1: 0.7423

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
0.9672	0.1546	100	0.7851	0.7256	0.4082
0.723	0.3091	200	0.6540	0.7633	0.5104
0.6837	0.4637	300	0.6314	0.7546	0.5525
0.617	0.6182	400	0.5643	0.7865	0.5749
0.5646	0.7728	500	0.5438	0.7961	0.5855
0.5512	0.9274	600	0.5321	0.8029	0.5996
0.5424	1.0819	700	0.5225	0.8077	0.6486
0.4391	1.2365	800	0.5219	0.8077	0.6325
0.4898	1.3910	900	0.5025	0.8164	0.6495
0.4858	1.5456	1000	0.4951	0.8232	0.6762
0.4845	1.7002	1100	0.4921	0.8261	0.6952
0.4459	1.8547	1200	0.4736	0.8425	0.7337
0.4382	2.0093	1300	0.4817	0.8087	0.6545
0.4137	2.1638	1400	0.4742	0.8357	0.7303
0.3781	2.3184	1500	0.4939	0.8213	0.6920
0.4351	2.4730	1600	0.4945	0.8309	0.7283
0.4155	2.6275	1700	0.4806	0.8367	0.7150
0.3674	2.7821	1800	0.4797	0.8357	0.7203
0.3878	2.9366	1900	0.4717	0.8406	0.7355
0.3613	3.0912	2000	0.4934	0.8367	0.7222
0.3289	3.2457	2100	0.4841	0.8377	0.7210
0.3332	3.4003	2200	0.4928	0.8386	0.7343
0.3724	3.5549	2300	0.4890	0.8386	0.7276
0.3421	3.7094	2400	0.4942	0.8425	0.7397
0.3289	3.8640	2500	0.4912	0.8454	0.7453
0.3374	4.0185	2600	0.4880	0.8454	0.7479
0.3045	4.1731	2700	0.4936	0.8415	0.7412
0.2781	4.3277	2800	0.4889	0.8444	0.7418
0.3461	4.4822	2900	0.5148	0.8444	0.7407
0.3228	4.6368	3000	0.5109	0.8338	0.7382
0.3222	4.7913	3100	0.5131	0.8473	0.7404
0.2552	4.9459	3200	0.5233	0.8435	0.7420
0.2804	5.1005	3300	0.5154	0.8435	0.7377
0.2257	5.2550	3400	0.5353	0.8357	0.7421
0.2731	5.4096	3500	0.5389	0.8512	0.7527
0.2638	5.5641	3600	0.5288	0.8512	0.7615
0.2674	5.7187	3700	0.5350	0.8483	0.7555
0.2701	5.8733	3800	0.5597	0.8454	0.7511
0.2733	6.0278	3900	0.5444	0.8367	0.7452
0.2106	6.1824	4000	0.5750	0.8261	0.7330
0.2631	6.3369	4100	0.5724	0.8338	0.7343
0.2227	6.4915	4200	0.5836	0.8145	0.7170
0.241	6.6461	4300	0.5633	0.8464	0.7544
0.2374	6.8006	4400	0.5758	0.8367	0.7437
0.2583	6.9552	4500	0.5661	0.8464	0.7475
0.2057	7.1097	4600	0.5843	0.8473	0.7521
0.1991	7.2643	4700	0.5905	0.8454	0.7466
0.1997	7.4189	4800	0.5916	0.8377	0.7421
0.1897	7.5734	4900	0.5918	0.8386	0.7454
0.2295	7.7280	5000	0.5916	0.8329	0.7391
0.2219	7.8825	5100	0.6007	0.8464	0.7511
0.2162	8.0371	5200	0.6132	0.8512	0.7550
0.1922	8.1917	5300	0.6047	0.8406	0.7474
0.206	8.3462	5400	0.6075	0.8473	0.7521
0.188	8.5008	5500	0.6161	0.8454	0.7474
0.2061	8.6553	5600	0.6218	0.8473	0.7494
0.2027	8.8099	5700	0.6237	0.8357	0.7382
0.2109	8.9645	5800	0.6226	0.8406	0.7462
0.2151	9.1190	5900	0.6235	0.8406	0.7461
0.1532	9.2736	6000	0.6253	0.8435	0.7470
0.1746	9.4281	6100	0.6257	0.8406	0.7440
0.1917	9.5827	6200	0.6288	0.8377	0.7411
0.1925	9.7372	6300	0.6298	0.8357	0.7395
0.1886	9.8918	6400	0.6299	0.8386	0.7423

Framework versions

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