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	4	7.7291	0.0	7.7292	2.7801
No log	2.0	8	4.4333	0.0088	4.4336	2.1056
No log	3.0	12	2.8053	0.0	2.8056	1.6750
No log	4.0	16	1.9297	0.0318	1.9302	1.3893
No log	5.0	20	1.5904	0.0431	1.5907	1.2612
No log	6.0	24	0.9328	0.0213	0.9331	0.9660
No log	7.0	28	1.7760	0.1776	1.7761	1.3327
No log	8.0	32	1.3768	0.1278	1.3769	1.1734
No log	9.0	36	1.1273	0.1676	1.1272	1.0617
No log	10.0	40	1.0641	0.2327	1.0637	1.0313
No log	11.0	44	0.9319	0.2791	0.9310	0.9649
No log	12.0	48	0.9888	0.2764	0.9879	0.9939
No log	13.0	52	1.1808	0.2892	1.1797	1.0862
No log	14.0	56	2.7178	0.1169	2.7157	1.6479
No log	15.0	60	0.9276	0.3620	0.9262	0.9624
No log	16.0	64	1.4092	0.3081	1.4079	1.1866
No log	17.0	68	0.9437	0.4094	0.9424	0.9708
No log	18.0	72	2.1342	0.1848	2.1329	1.4604
No log	19.0	76	0.8426	0.4211	0.8416	0.9174
No log	20.0	80	2.1361	0.1732	2.1350	1.4612
No log	21.0	84	0.8343	0.4126	0.8334	0.9129
No log	22.0	88	1.5523	0.2462	1.5514	1.2456
No log	23.0	92	1.2289	0.3062	1.2282	1.1082
No log	24.0	96	1.1965	0.3094	1.1959	1.0935
No log	25.0	100	0.9213	0.3817	0.9208	0.9596
No log	26.0	104	1.1129	0.3564	1.1122	1.0546
No log	27.0	108	1.6635	0.2504	1.6629	1.2895
No log	28.0	112	0.8765	0.3908	0.8760	0.9359
No log	29.0	116	0.9746	0.3390	0.9743	0.9871
No log	30.0	120	1.2904	0.2812	1.2899	1.1357
No log	31.0	124	1.1506	0.3095	1.1502	1.0725
No log	32.0	128	1.2422	0.3105	1.2415	1.1142
No log	33.0	132	0.8594	0.4249	0.8588	0.9267
No log	34.0	136	1.3640	0.2393	1.3636	1.1677
No log	35.0	140	0.7615	0.4627	0.7610	0.8723
No log	36.0	144	1.4426	0.2622	1.4420	1.2008
No log	37.0	148	0.8601	0.4050	0.8596	0.9271
No log	38.0	152	0.8065	0.4279	0.8061	0.8978
No log	39.0	156	1.0803	0.3276	1.0798	1.0392
No log	40.0	160	0.7966	0.4633	0.7961	0.8922
No log	41.0	164	1.5190	0.2771	1.5186	1.2323
No log	42.0	168	0.8622	0.4403	0.8618	0.9283
No log	43.0	172	1.3046	0.3127	1.3043	1.1421
No log	44.0	176	0.7954	0.4253	0.7951	0.8917
No log	45.0	180	1.1293	0.3166	1.1289	1.0625
No log	46.0	184	0.8225	0.4124	0.8220	0.9066
No log	47.0	188	0.7981	0.4106	0.7976	0.8931
No log	48.0	192	1.1755	0.3016	1.1750	1.0840
No log	49.0	196	0.9370	0.3653	0.9365	0.9677
No log	50.0	200	1.0830	0.3295	1.0825	1.0405
No log	51.0	204	0.7542	0.4347	0.7538	0.8682
No log	52.0	208	1.2578	0.3141	1.2573	1.1213
No log	53.0	212	0.8428	0.4229	0.8422	0.9177
No log	54.0	216	1.1466	0.3386	1.1461	1.0706
No log	55.0	220	0.7342	0.4509	0.7338	0.8566
No log	56.0	224	0.8137	0.4260	0.8133	0.9018
No log	57.0	228	1.0165	0.3275	1.0160	1.0080
No log	58.0	232	0.7342	0.4386	0.7338	0.8566
No log	59.0	236	1.0260	0.3363	1.0255	1.0127
No log	60.0	240	0.8505	0.3967	0.8499	0.9219

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV6_k4_task1_organization_k4_k4_fold2

ASAP_FineTuningBERT_AugV6_k4_task1_organization_k4_k4_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV6_k4_task1_organization_k4_k4_fold2