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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	2	9.3221	0.0018	9.3223	3.0532
No log	2.0	4	7.9932	0.0	7.9934	2.8273
No log	3.0	6	6.4319	0.0	6.4320	2.5361
No log	4.0	8	5.1796	0.0113	5.1799	2.2759
No log	5.0	10	4.1521	0.0078	4.1524	2.0377
No log	6.0	12	3.3006	0.0	3.3009	1.8168
No log	7.0	14	2.5139	0.0203	2.5142	1.5856
No log	8.0	16	1.9739	0.1154	1.9742	1.4051
No log	9.0	18	1.5517	0.0449	1.5521	1.2458
No log	10.0	20	1.2504	0.0213	1.2509	1.1184
No log	11.0	22	1.2033	0.0107	1.2038	1.0972
No log	12.0	24	0.8639	0.1335	0.8643	0.9297
No log	13.0	26	0.9116	0.1152	0.9120	0.9550
No log	14.0	28	0.7663	0.3426	0.7667	0.8756
No log	15.0	30	0.6699	0.4244	0.6701	0.8186
No log	16.0	32	0.8673	0.2811	0.8676	0.9314
No log	17.0	34	0.6033	0.4475	0.6035	0.7768
No log	18.0	36	0.6725	0.4859	0.6728	0.8202
No log	19.0	38	0.5052	0.4831	0.5051	0.7107
No log	20.0	40	0.6125	0.5035	0.6127	0.7827
No log	21.0	42	0.5936	0.5143	0.5938	0.7706
No log	22.0	44	0.6039	0.4704	0.6037	0.7770
No log	23.0	46	0.5784	0.5570	0.5785	0.7606
No log	24.0	48	0.6610	0.4981	0.6607	0.8128
No log	25.0	50	0.5952	0.5484	0.5951	0.7714
No log	26.0	52	0.5863	0.5464	0.5862	0.7656
No log	27.0	54	0.6831	0.5015	0.6827	0.8263
No log	28.0	56	0.6264	0.5551	0.6262	0.7913
No log	29.0	58	0.6079	0.5467	0.6076	0.7795
No log	30.0	60	0.7835	0.5254	0.7831	0.8849
No log	31.0	62	0.6452	0.5442	0.6450	0.8031
No log	32.0	64	0.6722	0.4934	0.6720	0.8197
No log	33.0	66	0.5987	0.5138	0.5984	0.7736
No log	34.0	68	0.6511	0.5251	0.6509	0.8068
No log	35.0	70	0.6183	0.5536	0.6180	0.7861
No log	36.0	72	0.6556	0.5211	0.6553	0.8095
No log	37.0	74	0.6309	0.5245	0.6307	0.7941
No log	38.0	76	0.6059	0.5299	0.6056	0.7782
No log	39.0	78	0.5954	0.5194	0.5952	0.7715
No log	40.0	80	0.5689	0.5035	0.5687	0.7541
No log	41.0	82	0.6160	0.5248	0.6157	0.7847
No log	42.0	84	0.5785	0.5622	0.5782	0.7604
No log	43.0	86	0.6030	0.5386	0.6028	0.7764
No log	44.0	88	0.5986	0.5460	0.5983	0.7735
No log	45.0	90	0.5773	0.5360	0.5770	0.7596
No log	46.0	92	0.6537	0.5041	0.6535	0.8084
No log	47.0	94	0.6422	0.5112	0.6420	0.8012
No log	48.0	96	0.6397	0.5302	0.6394	0.7996
No log	49.0	98	0.7972	0.5140	0.7969	0.8927
No log	50.0	100	0.5593	0.5553	0.5590	0.7477
No log	51.0	102	0.6562	0.5168	0.6560	0.8099
No log	52.0	104	0.5643	0.5237	0.5641	0.7511
No log	53.0	106	0.6785	0.4726	0.6785	0.8237
No log	54.0	108	0.6017	0.4630	0.6016	0.7757
No log	55.0	110	0.5750	0.5045	0.5749	0.7582
No log	56.0	112	0.5590	0.4993	0.5589	0.7476
No log	57.0	114	0.5876	0.5033	0.5874	0.7665
No log	58.0	116	0.5508	0.5465	0.5506	0.7420
No log	59.0	118	0.5523	0.5564	0.5520	0.7430
No log	60.0	120	0.5235	0.5593	0.5232	0.7233
No log	61.0	122	0.5702	0.5343	0.5699	0.7549
No log	62.0	124	0.5473	0.5036	0.5471	0.7396
No log	63.0	126	0.5320	0.5125	0.5318	0.7293
No log	64.0	128	0.5568	0.4969	0.5566	0.7461
No log	65.0	130	0.5492	0.4983	0.5491	0.7410
No log	66.0	132	0.6017	0.5018	0.6015	0.7756
No log	67.0	134	0.5487	0.5256	0.5486	0.7406
No log	68.0	136	0.5415	0.5307	0.5415	0.7358
No log	69.0	138	0.5559	0.5115	0.5558	0.7455
No log	70.0	140	0.5313	0.5079	0.5313	0.7289
No log	71.0	142	0.5531	0.5174	0.5531	0.7437
No log	72.0	144	0.5221	0.5358	0.5220	0.7225

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version23NewTestASAP_FineTuningBERT_AugV23_k3_task1_organization_k3_k3_fold2

Version23NewTestASAP_FineTuningBERT_AugV23_k3_task1_organization_k3_k3_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version23NewTestASAP_FineTuningBERT_AugV23_k3_task1_organization_k3_k3_fold2