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	3	8.1337	0.0	8.1337	2.8520
No log	2.0	6	6.7959	0.0	6.7959	2.6069
No log	3.0	9	5.4928	0.0112	5.4928	2.3437
No log	4.0	12	4.2238	0.0039	4.2238	2.0552
No log	5.0	15	3.0763	0.0	3.0763	1.7539
No log	6.0	18	2.0760	0.0689	2.0760	1.4408
No log	7.0	21	1.4154	0.0316	1.4154	1.1897
No log	8.0	24	1.0150	0.0316	1.0150	1.0075
No log	9.0	27	0.7662	0.3668	0.7662	0.8753
No log	10.0	30	0.6845	0.4416	0.6845	0.8273
No log	11.0	33	0.7858	0.3990	0.7858	0.8864
No log	12.0	36	0.5488	0.5126	0.5488	0.7408
No log	13.0	39	0.6093	0.5163	0.6093	0.7806
No log	14.0	42	0.5610	0.4782	0.5610	0.7490
No log	15.0	45	0.5003	0.5354	0.5003	0.7074
No log	16.0	48	0.6115	0.5244	0.6115	0.7820
No log	17.0	51	0.6456	0.4443	0.6456	0.8035
No log	18.0	54	0.6343	0.5341	0.6343	0.7964
No log	19.0	57	0.7135	0.4447	0.7135	0.8447
No log	20.0	60	0.5761	0.5515	0.5761	0.7590
No log	21.0	63	0.5637	0.5362	0.5637	0.7508
No log	22.0	66	0.5764	0.5580	0.5764	0.7592
No log	23.0	69	0.5561	0.5327	0.5561	0.7457
No log	24.0	72	0.6990	0.4956	0.6990	0.8361
No log	25.0	75	0.5519	0.5923	0.5519	0.7429
No log	26.0	78	0.6083	0.5720	0.6083	0.7799
No log	27.0	81	0.6195	0.5570	0.6195	0.7871
No log	28.0	84	0.6089	0.5824	0.6089	0.7803
No log	29.0	87	0.6410	0.5629	0.6410	0.8006
No log	30.0	90	0.6444	0.5548	0.6444	0.8027
No log	31.0	93	0.6511	0.5508	0.6511	0.8069
No log	32.0	96	0.6175	0.5768	0.6175	0.7858
No log	33.0	99	0.7971	0.5201	0.7971	0.8928
No log	34.0	102	0.7225	0.5462	0.7225	0.8500
No log	35.0	105	0.7005	0.5582	0.7005	0.8369
No log	36.0	108	0.6988	0.5605	0.6988	0.8360
No log	37.0	111	0.6564	0.5893	0.6564	0.8102
No log	38.0	114	0.8048	0.5387	0.8048	0.8971
No log	39.0	117	0.7019	0.5834	0.7019	0.8378
No log	40.0	120	1.0560	0.4633	1.0560	1.0276
No log	41.0	123	0.6248	0.6004	0.6248	0.7904
No log	42.0	126	0.8740	0.5125	0.8740	0.9349
No log	43.0	129	0.8288	0.5218	0.8288	0.9104
No log	44.0	132	0.6442	0.5982	0.6442	0.8026
No log	45.0	135	0.9067	0.4650	0.9067	0.9522
No log	46.0	138	0.6273	0.5979	0.6273	0.7920
No log	47.0	141	0.8039	0.5134	0.8039	0.8966
No log	48.0	144	0.7528	0.4989	0.7528	0.8677
No log	49.0	147	0.7770	0.5332	0.7770	0.8815
No log	50.0	150	0.8742	0.5218	0.8742	0.9350
No log	51.0	153	0.6670	0.5942	0.6670	0.8167
No log	52.0	156	1.1675	0.4559	1.1675	1.0805
No log	53.0	159	0.6892	0.6036	0.6892	0.8302
No log	54.0	162	0.6544	0.5895	0.6544	0.8090
No log	55.0	165	0.8674	0.5406	0.8674	0.9313
No log	56.0	168	0.7065	0.5635	0.7065	0.8405
No log	57.0	171	0.6477	0.6045	0.6477	0.8048
No log	58.0	174	0.9676	0.5186	0.9676	0.9837
No log	59.0	177	0.6782	0.5781	0.6782	0.8235
No log	60.0	180	0.7491	0.5500	0.7491	0.8655
No log	61.0	183	0.9436	0.5048	0.9436	0.9714
No log	62.0	186	0.7207	0.5627	0.7207	0.8490
No log	63.0	189	0.8462	0.5160	0.8462	0.9199
No log	64.0	192	0.8341	0.5144	0.8341	0.9133
No log	65.0	195	0.6939	0.5758	0.6939	0.8330
No log	66.0	198	0.8485	0.5234	0.8485	0.9211
No log	67.0	201	0.6970	0.5646	0.6970	0.8349
No log	68.0	204	0.6491	0.5998	0.6491	0.8057
No log	69.0	207	0.9288	0.5176	0.9288	0.9638
No log	70.0	210	0.6504	0.5983	0.6504	0.8065
No log	71.0	213	0.8592	0.5214	0.8592	0.9269
No log	72.0	216	0.8376	0.5352	0.8376	0.9152
No log	73.0	219	0.8187	0.5589	0.8187	0.9048
No log	74.0	222	1.0614	0.4795	1.0614	1.0303
No log	75.0	225	0.8826	0.5231	0.8826	0.9395
No log	76.0	228	0.9009	0.5203	0.9009	0.9492
No log	77.0	231	0.7517	0.5835	0.7517	0.8670
No log	78.0	234	0.9721	0.5077	0.9721	0.9859
No log	79.0	237	0.7743	0.5733	0.7743	0.8800
No log	80.0	240	0.8081	0.5525	0.8081	0.8990
No log	81.0	243	1.0271	0.4771	1.0271	1.0135
No log	82.0	246	0.7523	0.5819	0.7523	0.8674
No log	83.0	249	0.9307	0.5188	0.9307	0.9647
No log	84.0	252	0.8023	0.5598	0.8023	0.8957
No log	85.0	255	0.8736	0.5317	0.8736	0.9346
No log	86.0	258	0.8593	0.5337	0.8593	0.9270
No log	87.0	261	0.8523	0.5376	0.8523	0.9232

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_fold0

Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_fold0