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	8.9329	0.0	8.9329	2.9888
No log	2.0	4	8.1515	0.0	8.1515	2.8551
No log	3.0	6	6.8301	0.0	6.8301	2.6135
No log	4.0	8	6.4891	-0.0191	6.4891	2.5474
No log	5.0	10	5.4540	0.0112	5.4540	2.3354
No log	6.0	12	4.7029	0.0039	4.7029	2.1686
No log	7.0	14	3.9318	0.0	3.9318	1.9829
No log	8.0	16	3.1791	0.0	3.1791	1.7830
No log	9.0	18	2.5232	0.0434	2.5232	1.5884
No log	10.0	20	1.9844	0.0484	1.9844	1.4087
No log	11.0	22	1.7364	0.0548	1.7364	1.3177
No log	12.0	24	1.4445	0.0419	1.4445	1.2019
No log	13.0	26	1.1628	0.0316	1.1628	1.0783
No log	14.0	28	0.9896	0.0316	0.9896	0.9948
No log	15.0	30	1.1226	0.0548	1.1226	1.0596
No log	16.0	32	0.9765	0.0419	0.9765	0.9882
No log	17.0	34	0.8741	0.0078	0.8741	0.9349
No log	18.0	36	0.7674	0.2999	0.7674	0.8760
No log	19.0	38	0.7324	0.4290	0.7324	0.8558
No log	20.0	40	1.0009	0.1710	1.0009	1.0005
No log	21.0	42	0.8312	0.3626	0.8312	0.9117
No log	22.0	44	0.5538	0.5158	0.5538	0.7442
No log	23.0	46	0.6734	0.4861	0.6734	0.8206
No log	24.0	48	1.2214	0.3066	1.2214	1.1052
No log	25.0	50	0.7452	0.4353	0.7452	0.8633
No log	26.0	52	0.5676	0.4292	0.5676	0.7534
No log	27.0	54	0.5923	0.4194	0.5923	0.7696
No log	28.0	56	0.8067	0.3883	0.8067	0.8981
No log	29.0	58	0.9584	0.3482	0.9584	0.9790
No log	30.0	60	0.6407	0.4104	0.6407	0.8005
No log	31.0	62	0.6835	0.4287	0.6835	0.8267
No log	32.0	64	0.6354	0.4894	0.6354	0.7971
No log	33.0	66	0.6903	0.4395	0.6903	0.8309
No log	34.0	68	0.7450	0.4594	0.7450	0.8631
No log	35.0	70	0.6745	0.5314	0.6745	0.8213
No log	36.0	72	0.8506	0.4081	0.8506	0.9223
No log	37.0	74	0.8715	0.4124	0.8715	0.9335
No log	38.0	76	0.6062	0.5371	0.6062	0.7786
No log	39.0	78	0.6797	0.4854	0.6797	0.8245
No log	40.0	80	0.6087	0.5513	0.6087	0.7802
No log	41.0	82	0.7305	0.5226	0.7305	0.8547
No log	42.0	84	0.6598	0.5624	0.6598	0.8123
No log	43.0	86	0.7966	0.4851	0.7966	0.8926
No log	44.0	88	0.7100	0.5339	0.7100	0.8426
No log	45.0	90	0.6237	0.5526	0.6237	0.7897
No log	46.0	92	0.6372	0.5360	0.6372	0.7982
No log	47.0	94	0.6238	0.5279	0.6238	0.7898
No log	48.0	96	0.6475	0.5569	0.6475	0.8047
No log	49.0	98	0.6742	0.5441	0.6742	0.8211
No log	50.0	100	0.7039	0.5143	0.7039	0.8390
No log	51.0	102	0.7138	0.5093	0.7138	0.8449
No log	52.0	104	0.7381	0.5401	0.7381	0.8591
No log	53.0	106	0.7451	0.5501	0.7451	0.8632
No log	54.0	108	0.6680	0.5280	0.6680	0.8173
No log	55.0	110	0.7419	0.4965	0.7419	0.8614
No log	56.0	112	0.7135	0.4947	0.7135	0.8447
No log	57.0	114	0.6567	0.5478	0.6567	0.8104
No log	58.0	116	0.6915	0.5583	0.6915	0.8316
No log	59.0	118	0.6774	0.5304	0.6774	0.8230
No log	60.0	120	0.6946	0.5346	0.6946	0.8334
No log	61.0	122	0.7031	0.5494	0.7031	0.8385
No log	62.0	124	0.7032	0.5186	0.7032	0.8386

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV15_k1_task1_organization_k1_k1_fold0

ASAP_FineTuningBERT_AugV15_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV15_k1_task1_organization_k1_k1_fold0