Czert_fine_tuned_cs_wikann

This model is a fine-tuned version of UWB-AIR/Czert-B-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2400
• Precision: 0.9088
• Recall: 0.9287
• F1: 0.9186
• Accuracy: 0.9683

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: 8
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3209	0.2	500	0.2147	0.7711	0.8252	0.7972	0.9393
0.197	0.4	1000	0.1891	0.8147	0.8565	0.8351	0.9493
0.1774	0.6	1500	0.1887	0.8267	0.8608	0.8434	0.9496
0.1647	0.8	2000	0.1482	0.8396	0.8947	0.8663	0.9564
0.1592	1.0	2500	0.1489	0.8687	0.8977	0.8830	0.9600
0.0869	1.2	3000	0.1694	0.8706	0.9062	0.8880	0.9616
0.0935	1.4	3500	0.1547	0.8850	0.9116	0.8981	0.9638
0.0973	1.6	4000	0.1478	0.8723	0.9077	0.8896	0.9638
0.1047	1.8	4500	0.1446	0.8891	0.9048	0.8969	0.9632
0.0938	2.0	5000	0.1419	0.8785	0.9125	0.8952	0.9639
0.0496	2.2	5500	0.1775	0.8779	0.9172	0.8971	0.9639
0.054	2.4	6000	0.1620	0.8996	0.9113	0.9054	0.9650
0.0532	2.6	6500	0.1717	0.8918	0.9176	0.9045	0.9652
0.0527	2.8	7000	0.1774	0.8978	0.9216	0.9096	0.9658
0.0576	3.0	7500	0.1678	0.9013	0.9198	0.9105	0.9674
0.027	3.2	8000	0.1844	0.9004	0.9212	0.9107	0.9674
0.0356	3.4	8500	0.1700	0.8952	0.9203	0.9076	0.9672
0.0315	3.6	9000	0.1864	0.9012	0.9230	0.9120	0.9667
0.0307	3.8	9500	0.1886	0.8968	0.9238	0.9101	0.9662
0.0288	4.0	10000	0.1985	0.8990	0.9244	0.9115	0.9669
0.0141	4.2	10500	0.2069	0.8953	0.9260	0.9104	0.9672
0.0187	4.4	11000	0.2051	0.9024	0.9207	0.9114	0.9668
0.0169	4.6	11500	0.2010	0.9036	0.9252	0.9143	0.9677
0.0233	4.8	12000	0.2028	0.8978	0.9268	0.9120	0.9671
0.02	5.0	12500	0.1958	0.9042	0.9263	0.9151	0.9678
0.0089	5.2	13000	0.2155	0.9043	0.9267	0.9154	0.9675
0.0084	5.4	13500	0.2289	0.9047	0.9285	0.9164	0.9674
0.0146	5.6	14000	0.2195	0.9032	0.9281	0.9155	0.9681
0.0066	5.8	14500	0.2239	0.9064	0.9280	0.9171	0.9681
0.01	6.0	15000	0.2256	0.9084	0.9270	0.9176	0.9681
0.0034	6.2	15500	0.2323	0.9065	0.9284	0.9173	0.9678
0.0065	6.4	16000	0.2339	0.9088	0.9277	0.9182	0.9680
0.0041	6.6	16500	0.2384	0.9086	0.9283	0.9184	0.9679
0.0038	6.8	17000	0.2396	0.9095	0.9287	0.9190	0.9683
0.0044	7.0	17500	0.2400	0.9088	0.9287	0.9186	0.9683

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0