CNEC_1_1_Supertypes_robeczech-base

This model is a fine-tuned version of ufal/robeczech-base on the cnec dataset. It achieves the following results on the evaluation set:

• Loss: 0.2799
• Precision: 0.8446
• Recall: 0.8912
• F1: 0.8673
• Accuracy: 0.9518

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.0614	1.7	500	0.6385	0.2880	0.1057	0.1546	0.8234
0.5512	3.4	1000	0.3567	0.7105	0.7542	0.7317	0.9197
0.3472	5.1	1500	0.2644	0.7602	0.8254	0.7914	0.9342
0.2659	6.8	2000	0.2466	0.7945	0.8492	0.8209	0.9389
0.2169	8.5	2500	0.2240	0.8252	0.8621	0.8432	0.9453
0.1797	10.2	3000	0.2113	0.8345	0.8714	0.8525	0.9487
0.1609	11.9	3500	0.2178	0.8213	0.8815	0.8503	0.9487
0.1371	13.61	4000	0.2126	0.8406	0.8811	0.8603	0.9509
0.1237	15.31	4500	0.2127	0.8422	0.8775	0.8595	0.9510
0.1101	17.01	5000	0.2065	0.8520	0.8855	0.8684	0.9538
0.0988	18.71	5500	0.2113	0.8457	0.8895	0.8671	0.9534
0.0904	20.41	6000	0.2280	0.8390	0.8895	0.8635	0.9523
0.0831	22.11	6500	0.2268	0.8430	0.8948	0.8681	0.9532
0.0758	23.81	7000	0.2472	0.8396	0.8864	0.8624	0.9502
0.0713	25.51	7500	0.2377	0.8402	0.8877	0.8633	0.9511
0.066	27.21	8000	0.2533	0.8346	0.8855	0.8593	0.9495
0.0591	28.91	8500	0.2449	0.8494	0.8926	0.8704	0.9527
0.0601	30.61	9000	0.2503	0.8421	0.8890	0.8649	0.9527
0.0528	32.31	9500	0.2605	0.8474	0.8935	0.8698	0.9514
0.051	34.01	10000	0.2677	0.8389	0.8886	0.8630	0.9511
0.0462	35.71	10500	0.2628	0.8391	0.8921	0.8648	0.9513
0.0438	37.41	11000	0.2629	0.8457	0.8939	0.8691	0.9526
0.0423	39.12	11500	0.2673	0.8406	0.8930	0.8660	0.9502
0.0395	40.82	12000	0.2700	0.8423	0.8904	0.8657	0.9518
0.0386	42.52	12500	0.2716	0.8486	0.8943	0.8709	0.9528
0.0384	44.22	13000	0.2727	0.8465	0.8921	0.8687	0.9523
0.0352	45.92	13500	0.2741	0.8494	0.8926	0.8704	0.9526
0.0351	47.62	14000	0.2776	0.8469	0.8926	0.8691	0.9520
0.0327	49.32	14500	0.2799	0.8446	0.8912	0.8673	0.9518

Framework versions

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