ASAP_FineTuningBERT_AugV8_k10_task1_organization_fold3

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.9359
• Qwk: 0.1548
• Mse: 1.9372
• Rmse: 1.3918

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: 64
• eval_batch_size: 64
• 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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.25	2	11.3660	0.0117	11.3650	3.3712
No log	0.5	4	10.2282	0.0	10.2275	3.1980
No log	0.75	6	8.2858	0.0	8.2852	2.8784
No log	1.0	8	6.6621	0.0	6.6617	2.5810
6.5469	1.25	10	5.1710	0.0329	5.1706	2.2739
6.5469	1.5	12	4.0013	0.0076	4.0010	2.0003
6.5469	1.75	14	2.8683	0.0	2.8685	1.6937
6.5469	2.0	16	2.1810	0.0853	2.1812	1.4769
6.5469	2.25	18	1.5909	0.0329	1.5914	1.2615
2.3647	2.5	20	1.5947	0.0129	1.5955	1.2631
2.3647	2.75	22	1.4075	0.0157	1.4083	1.1867
2.3647	3.0	24	1.1561	0.0102	1.1570	1.0756
2.3647	3.25	26	1.3992	0.0329	1.3999	1.1832
2.3647	3.5	28	1.7034	0.0926	1.7040	1.3054
1.7022	3.75	30	1.7301	0.1104	1.7308	1.3156
1.7022	4.0	32	1.1643	0.0452	1.1651	1.0794
1.7022	4.25	34	1.8599	0.1272	1.8606	1.3641
1.7022	4.5	36	1.5065	0.1093	1.5073	1.2277
1.7022	4.75	38	0.7897	0.3557	0.7905	0.8891
1.5456	5.0	40	1.2770	0.1380	1.2779	1.1304
1.5456	5.25	42	2.0538	0.1825	2.0547	1.4334
1.5456	5.5	44	0.8848	0.3190	0.8856	0.9411
1.5456	5.75	46	0.8883	0.3191	0.8891	0.9429
1.5456	6.0	48	1.6953	0.1913	1.6963	1.3024
1.2794	6.25	50	0.8889	0.3603	0.8896	0.9432
1.2794	6.5	52	1.1502	0.2700	1.1512	1.0729
1.2794	6.75	54	2.3053	0.0932	2.3064	1.5187
1.2794	7.0	56	1.0398	0.3160	1.0407	1.0201
1.2794	7.25	58	0.7600	0.4084	0.7605	0.8720
1.0687	7.5	60	1.9526	0.1243	1.9537	1.3977
1.0687	7.75	62	1.9734	0.1187	1.9744	1.4051
1.0687	8.0	64	0.7830	0.3927	0.7835	0.8851
1.0687	8.25	66	0.9158	0.3456	0.9164	0.9573
1.0687	8.5	68	1.8712	0.1380	1.8724	1.3684
0.7516	8.75	70	0.9993	0.3261	0.9999	1.0000
0.7516	9.0	72	0.6742	0.4639	0.6743	0.8212
0.7516	9.25	74	0.8835	0.4050	0.8840	0.9402
0.7516	9.5	76	2.2347	0.1095	2.2360	1.4953
0.7516	9.75	78	1.5217	0.2001	1.5229	1.2341
0.7336	10.0	80	0.7839	0.4550	0.7844	0.8856
0.7336	10.25	82	1.1400	0.2530	1.1411	1.0682
0.7336	10.5	84	2.7707	0.0328	2.7723	1.6650
0.7336	10.75	86	1.8539	0.0965	1.8553	1.3621
0.7336	11.0	88	0.8147	0.4035	0.8151	0.9028
0.5475	11.25	90	0.8105	0.4264	0.8108	0.9005
0.5475	11.5	92	1.9359	0.1548	1.9372	1.3918

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0