swin-CEMEDE-og

This model is a fine-tuned version of microsoft/swin-base-simmim-window6-192 on the cemede dataset. It achieves the following results on the evaluation set:

• Loss: 0.8707
• Accuracy: 0.8018
• F1: 0.7127

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: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use 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
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
2.2296	0.0840	100	2.1836	0.2570	0.0825
1.8921	0.1679	200	2.2063	0.3080	0.1300
1.682	0.2519	300	2.4050	0.3862	0.1449
1.8146	0.3359	400	1.9343	0.4253	0.2199
1.423	0.4198	500	1.9058	0.4579	0.2335
1.5637	0.5038	600	1.6756	0.5085	0.3331
1.0535	0.5877	700	1.4023	0.5641	0.3791
1.0955	0.6717	800	1.3086	0.6069	0.4426
0.8927	0.7557	900	1.2083	0.6377	0.5108
0.8035	0.8396	1000	1.3281	0.6340	0.4921
0.8517	0.9236	1100	1.2840	0.6492	0.5175
0.6035	1.0076	1200	1.2919	0.6446	0.5013
0.7727	1.0915	1300	1.0839	0.6878	0.5742
0.625	1.1755	1400	1.1132	0.7034	0.5552
0.554	1.2594	1500	1.2120	0.6492	0.5758
0.4117	1.3434	1600	1.1343	0.7030	0.5748
0.7557	1.4274	1700	1.1490	0.6975	0.5751
0.4841	1.5113	1800	0.9364	0.7756	0.6364
0.4899	1.5953	1900	1.1162	0.6929	0.5657
0.6598	1.6793	2000	0.9602	0.7402	0.6597
0.2826	1.7632	2100	1.2618	0.7044	0.6255
0.4785	1.8472	2200	1.0743	0.7269	0.6488
0.4427	1.9312	2300	0.8803	0.7641	0.6690
0.5305	2.0151	2400	0.8739	0.7830	0.6996
0.3814	2.0991	2500	0.9660	0.7789	0.6873
0.2273	2.1830	2600	1.0271	0.7789	0.7071
0.232	2.2670	2700	0.9957	0.7724	0.6961
0.2101	2.3510	2800	0.9729	0.7798	0.7196
0.4029	2.4349	2900	1.0296	0.7526	0.6911
0.2645	2.5189	3000	1.0878	0.7747	0.7058
0.3111	2.6029	3100	1.0745	0.7623	0.7072
0.1767	2.6868	3200	0.8820	0.7913	0.7424
0.167	2.7708	3300	0.8707	0.8018	0.7127
0.2523	2.8547	3400	1.0131	0.8046	0.7418
0.0786	2.9387	3500	1.0026	0.7807	0.7249
0.259	3.0227	3600	0.9817	0.7922	0.7109
0.3004	3.1066	3700	1.0838	0.7977	0.7341
0.1594	3.1906	3800	0.9184	0.8078	0.7323
0.1957	3.2746	3900	0.8777	0.8248	0.7255
0.1107	3.3585	4000	0.9186	0.8216	0.7360
0.1389	3.4425	4100	0.9996	0.8032	0.7358
0.1273	3.5264	4200	1.0062	0.8147	0.7604
0.2635	3.6104	4300	1.0976	0.8041	0.7406

Framework versions

• Transformers 4.55.4
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4