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	1.0	3	8.6608	0.0	8.6612	2.9430
No log	2.0	6	6.9396	0.0	6.9399	2.6344
No log	3.0	9	5.2474	0.0113	5.2478	2.2908
No log	4.0	12	4.0963	0.0	4.0966	2.0240
No log	5.0	15	3.0152	0.0	3.0156	1.7366
No log	6.0	18	2.3311	0.1286	2.3316	1.5269
No log	7.0	21	1.6350	0.0	1.6354	1.2788
No log	8.0	24	1.2090	0.0	1.2095	1.0998
No log	9.0	27	0.9666	0.0	0.9670	0.9834
No log	10.0	30	0.8366	0.1862	0.8371	0.9149
No log	11.0	33	0.8235	0.0430	0.8240	0.9077
No log	12.0	36	0.8693	0.0327	0.8696	0.9325
No log	13.0	39	1.1667	0.0	1.1671	1.0803
No log	14.0	42	1.2646	0.1874	1.2647	1.1246
No log	15.0	45	1.4987	0.1452	1.4987	1.2242
No log	16.0	48	1.0102	0.3065	1.0099	1.0049
No log	17.0	51	1.4370	0.2970	1.4359	1.1983
No log	18.0	54	0.5881	0.5456	0.5875	0.7665
No log	19.0	57	1.0492	0.4885	1.0477	1.0236
No log	20.0	60	0.8822	0.5469	0.8808	0.9385
No log	21.0	63	1.1360	0.4116	1.1358	1.0657
No log	22.0	66	0.7505	0.5333	0.7501	0.8661
No log	23.0	69	1.1147	0.4751	1.1134	1.0552
No log	24.0	72	0.6408	0.5109	0.6406	0.8004
No log	25.0	75	0.8588	0.4336	0.8588	0.9267
No log	26.0	78	0.9970	0.4888	0.9959	0.9980
No log	27.0	81	1.4799	0.4007	1.4784	1.2159
No log	28.0	84	0.8211	0.5011	0.8207	0.9059
No log	29.0	87	0.9405	0.4651	0.9404	0.9697
No log	30.0	90	0.7191	0.5336	0.7186	0.8477
No log	31.0	93	0.9109	0.5366	0.9101	0.9540
No log	32.0	96	0.7637	0.5429	0.7632	0.8736
No log	33.0	99	0.7540	0.5571	0.7533	0.8679
No log	34.0	102	0.8787	0.5340	0.8779	0.9370
No log	35.0	105	0.7011	0.5713	0.7005	0.8370
No log	36.0	108	0.6134	0.6045	0.6130	0.7829
No log	37.0	111	0.7784	0.5477	0.7777	0.8818
No log	38.0	114	0.6930	0.6044	0.6925	0.8321
No log	39.0	117	0.8651	0.5409	0.8641	0.9296
No log	40.0	120	0.8806	0.5385	0.8797	0.9379
No log	41.0	123	0.7397	0.5742	0.7391	0.8597
No log	42.0	126	0.9371	0.4997	0.9363	0.9676
No log	43.0	129	0.7630	0.5706	0.7623	0.8731
No log	44.0	132	0.8502	0.5435	0.8494	0.9216
No log	45.0	135	0.8541	0.5417	0.8534	0.9238
No log	46.0	138	0.6403	0.6158	0.6398	0.7999
No log	47.0	141	0.7593	0.5748	0.7587	0.8710
No log	48.0	144	0.8216	0.5483	0.8208	0.9060
No log	49.0	147	0.7331	0.5622	0.7324	0.8558
No log	50.0	150	0.8350	0.5343	0.8342	0.9133
No log	51.0	153	0.7353	0.5585	0.7346	0.8571
No log	52.0	156	0.7409	0.5586	0.7402	0.8604
No log	53.0	159	0.7553	0.5508	0.7546	0.8687
No log	54.0	162	0.7561	0.5447	0.7554	0.8692
No log	55.0	165	0.7233	0.5474	0.7227	0.8501
No log	56.0	168	0.7600	0.5494	0.7593	0.8714
No log	57.0	171	0.7524	0.5495	0.7517	0.8670
No log	58.0	174	0.7317	0.5582	0.7311	0.8550
No log	59.0	177	0.7194	0.5625	0.7188	0.8478
No log	60.0	180	0.6546	0.5579	0.6542	0.8088
No log	61.0	183	0.7442	0.5579	0.7437	0.8624
No log	62.0	186	0.6993	0.5694	0.6988	0.8359
No log	63.0	189	0.7542	0.5472	0.7536	0.8681
No log	64.0	192	0.7385	0.5578	0.7380	0.8591
No log	65.0	195	0.7189	0.5683	0.7184	0.8476
No log	66.0	198	0.6864	0.5714	0.6859	0.8282

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold2

ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold2