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	2	8.7860	0.0	8.7842	2.9638
No log	2.0	4	7.9340	0.0	7.9323	2.8164
No log	3.0	6	6.3746	0.0017	6.3732	2.5245
No log	4.0	8	5.2251	0.0259	5.2236	2.2855
No log	5.0	10	4.1886	0.0	4.1874	2.0463
No log	6.0	12	3.3131	0.0	3.3120	1.8199
No log	7.0	14	2.7223	0.0003	2.7213	1.6496
No log	8.0	16	2.1688	0.0694	2.1678	1.4724
No log	9.0	18	2.2349	0.0655	2.2340	1.4947
No log	10.0	20	1.5520	0.0302	1.5511	1.2454
No log	11.0	22	1.3795	0.0761	1.3790	1.1743
No log	12.0	24	1.4921	0.1143	1.4917	1.2214
No log	13.0	26	1.5098	0.1159	1.5095	1.2286
No log	14.0	28	1.0517	0.0843	1.0513	1.0253
No log	15.0	30	0.9096	0.0960	0.9092	0.9535
No log	16.0	32	1.3212	0.1607	1.3212	1.1494
No log	17.0	34	1.6700	0.1770	1.6700	1.2923
No log	18.0	36	1.1169	0.2027	1.1170	1.0569
No log	19.0	38	1.1502	0.3673	1.1504	1.0726
No log	20.0	40	1.4534	0.2957	1.4537	1.2057
No log	21.0	42	1.6251	0.2441	1.6255	1.2749
No log	22.0	44	0.9701	0.3927	0.9705	0.9851
No log	23.0	46	0.8648	0.4271	0.8650	0.9301
No log	24.0	48	0.9889	0.3672	0.9894	0.9947
No log	25.0	50	1.1700	0.2852	1.1705	1.0819
No log	26.0	52	0.9759	0.3709	0.9764	0.9881
No log	27.0	54	0.7303	0.4271	0.7306	0.8547
No log	28.0	56	0.7742	0.4321	0.7745	0.8801
No log	29.0	58	1.2123	0.3169	1.2126	1.1012
No log	30.0	60	0.9314	0.3826	0.9320	0.9654
No log	31.0	62	0.7183	0.4358	0.7188	0.8478
No log	32.0	64	0.8561	0.4042	0.8569	0.9257
No log	33.0	66	0.7965	0.4215	0.7972	0.8929
No log	34.0	68	0.7414	0.4006	0.7422	0.8615
No log	35.0	70	0.8184	0.4116	0.8193	0.9052
No log	36.0	72	0.9375	0.3714	0.9384	0.9687
No log	37.0	74	0.9005	0.4408	0.9016	0.9495
No log	38.0	76	0.9895	0.3767	0.9904	0.9952
No log	39.0	78	1.4360	0.2567	1.4365	1.1985
No log	40.0	80	1.0352	0.3991	1.0357	1.0177
No log	41.0	82	0.8566	0.4713	0.8571	0.9258
No log	42.0	84	0.9178	0.4117	0.9180	0.9581
No log	43.0	86	1.1296	0.3533	1.1298	1.0629
No log	44.0	88	0.8868	0.4364	0.8871	0.9418
No log	45.0	90	0.9536	0.4382	0.9541	0.9768
No log	46.0	92	0.9742	0.4355	0.9748	0.9873
No log	47.0	94	0.9078	0.4342	0.9081	0.9529
No log	48.0	96	0.9904	0.3956	0.9907	0.9954
No log	49.0	98	0.8475	0.4923	0.8478	0.9208
No log	50.0	100	1.1055	0.4299	1.1059	1.0516
No log	51.0	102	1.0893	0.4353	1.0897	1.0439
No log	52.0	104	0.7938	0.5185	0.7940	0.8911
No log	53.0	106	0.9957	0.3930	0.9960	0.9980
No log	54.0	108	0.9673	0.3935	0.9676	0.9837
No log	55.0	110	0.7633	0.5349	0.7635	0.8738
No log	56.0	112	0.8974	0.4886	0.8976	0.9474
No log	57.0	114	0.9071	0.4990	0.9073	0.9525
No log	58.0	116	0.8458	0.4974	0.8460	0.9198
No log	59.0	118	0.8897	0.4520	0.8899	0.9433
No log	60.0	120	0.8268	0.5117	0.8269	0.9094
No log	61.0	122	0.8597	0.5259	0.8598	0.9273
No log	62.0	124	0.7661	0.5289	0.7663	0.8754
No log	63.0	126	0.7267	0.4987	0.7268	0.8525
No log	64.0	128	0.7086	0.4837	0.7087	0.8419
No log	65.0	130	0.7694	0.5169	0.7696	0.8772
No log	66.0	132	0.7809	0.5024	0.7810	0.8838
No log	67.0	134	0.7737	0.5030	0.7738	0.8797
No log	68.0	136	0.7801	0.4956	0.7803	0.8833
No log	69.0	138	0.7908	0.4966	0.7909	0.8893
No log	70.0	140	0.8347	0.5058	0.8347	0.9136
No log	71.0	142	0.8289	0.5153	0.8289	0.9104
No log	72.0	144	0.7807	0.5003	0.7808	0.8836
No log	73.0	146	0.7633	0.4797	0.7634	0.8737
No log	74.0	148	0.7347	0.5044	0.7348	0.8572
No log	75.0	150	0.7366	0.4837	0.7366	0.8583

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3

ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3