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	10.9200	-0.0188	10.9173	3.3041
No log	2.0	4	10.0080	0.0	10.0053	3.1631
No log	3.0	6	8.6752	0.0	8.6725	2.9449
No log	4.0	8	7.8751	0.0	7.8726	2.8058
No log	5.0	10	7.6323	0.0	7.6299	2.7622
No log	6.0	12	7.4222	0.0	7.4198	2.7239
No log	7.0	14	7.1130	0.0	7.1107	2.6666
No log	8.0	16	6.5029	0.0	6.5006	2.5496
No log	9.0	18	5.8423	-0.0414	5.8400	2.4166
No log	10.0	20	4.9859	0.0010	4.9837	2.2324
No log	11.0	22	3.7686	0.0	3.7666	1.9408
No log	12.0	24	3.0361	0.0	3.0342	1.7419
No log	13.0	26	2.6331	-0.0337	2.6312	1.6221
No log	14.0	28	1.9417	0.0217	1.9399	1.3928
No log	15.0	30	1.5439	0.0158	1.5422	1.2419
No log	16.0	32	1.4036	0.0	1.4020	1.1841
No log	17.0	34	1.2219	0.0	1.2204	1.1047
No log	18.0	36	1.1216	0.0	1.1202	1.0584
No log	19.0	38	1.1053	0.0105	1.1038	1.0506
No log	20.0	40	0.9869	0.1320	0.9855	0.9927
No log	21.0	42	1.0610	0.1526	1.0596	1.0294
No log	22.0	44	1.1517	0.1447	1.1502	1.0725
No log	23.0	46	1.2369	0.3175	1.2356	1.1116
No log	24.0	48	1.6387	0.3494	1.6374	1.2796
No log	25.0	50	1.2567	0.3056	1.2556	1.1205
No log	26.0	52	1.3646	0.0030	1.3631	1.1675
No log	27.0	54	1.1762	0.1208	1.1749	1.0839
No log	28.0	56	1.1091	0.4047	1.1079	1.0526
No log	29.0	58	0.9185	0.2899	0.9173	0.9578
No log	30.0	60	1.1573	-0.0400	1.1559	1.0751
No log	31.0	62	1.0180	0.0705	1.0167	1.0083
No log	32.0	64	0.9203	0.4125	0.9189	0.9586
No log	33.0	66	0.9798	0.3382	0.9784	0.9891
No log	34.0	68	1.2895	0.0451	1.2882	1.1350
No log	35.0	70	1.2946	0.0598	1.2934	1.1373
No log	36.0	72	1.0219	0.3404	1.0206	1.0103
No log	37.0	74	1.0240	0.4152	1.0227	1.0113
No log	38.0	76	1.0067	0.3037	1.0056	1.0028
No log	39.0	78	0.9996	0.3262	0.9985	0.9993
No log	40.0	80	0.9789	0.4067	0.9777	0.9888
No log	41.0	82	1.1729	0.1792	1.1720	1.0826
No log	42.0	84	1.3030	0.1219	1.3021	1.1411
No log	43.0	86	1.2132	0.2413	1.2119	1.1009
No log	44.0	88	1.3737	0.1338	1.3727	1.1716
No log	45.0	90	1.4376	0.0964	1.4368	1.1987
No log	46.0	92	1.3585	0.1997	1.3575	1.1651
No log	47.0	94	1.2909	0.2865	1.2897	1.1356
No log	48.0	96	1.4282	0.1615	1.4273	1.1947
No log	49.0	98	1.4171	0.1697	1.4162	1.1900
No log	50.0	100	1.3692	0.2272	1.3681	1.1697
No log	51.0	102	1.4789	0.1589	1.4780	1.2157
No log	52.0	104	1.5048	0.1507	1.5039	1.2263
No log	53.0	106	1.4587	0.2059	1.4576	1.2073
No log	54.0	108	1.4037	0.2714	1.4025	1.1843
No log	55.0	110	1.4032	0.2685	1.4020	1.1841
No log	56.0	112	1.5446	0.1496	1.5436	1.2424
No log	57.0	114	1.4179	0.2533	1.4168	1.1903

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV8_k1_task1_organization_k1_k1_fold1

Version2ASAP_FineTuningBERT_AugV8_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV8_k1_task1_organization_k1_k1_fold1