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	3	11.3298	-0.0045	11.3270	3.3656
No log	2.0	6	9.1899	0.0	9.1876	3.0311
No log	3.0	9	6.3457	-0.0097	6.3430	2.5185
No log	4.0	12	3.8561	0.0128	3.8541	1.9632
No log	5.0	15	2.4416	0.0134	2.4398	1.5620
No log	6.0	18	1.6526	0.0276	1.6511	1.2850
No log	7.0	21	1.1619	0.0106	1.1606	1.0773
No log	8.0	24	1.0173	0.0065	1.0159	1.0079
No log	9.0	27	1.0276	-0.1857	1.0265	1.0132
No log	10.0	30	0.6901	0.3459	0.6892	0.8302
No log	11.0	33	1.2493	0.1886	1.2478	1.1171
No log	12.0	36	0.5618	0.4962	0.5608	0.7489
No log	13.0	39	0.5705	0.4903	0.5697	0.7548
No log	14.0	42	1.1981	0.2735	1.1964	1.0938
No log	15.0	45	0.8499	0.3935	0.8483	0.9210
No log	16.0	48	0.5805	0.5270	0.5794	0.7612
No log	17.0	51	1.1911	0.2857	1.1893	1.0905
No log	18.0	54	0.5939	0.5005	0.5927	0.7699
No log	19.0	57	0.9313	0.2999	0.9297	0.9642
No log	20.0	60	0.8545	0.3501	0.8530	0.9236
No log	21.0	63	0.6475	0.4599	0.6464	0.8040
No log	22.0	66	0.8075	0.4227	0.8060	0.8978
No log	23.0	69	0.5873	0.5075	0.5861	0.7656
No log	24.0	72	0.6841	0.4715	0.6826	0.8262
No log	25.0	75	0.7411	0.4592	0.7395	0.8600
No log	26.0	78	0.6271	0.4899	0.6259	0.7911
No log	27.0	81	0.7331	0.4886	0.7315	0.8553
No log	28.0	84	0.7361	0.4810	0.7351	0.8574
No log	29.0	87	0.7707	0.4568	0.7692	0.8771
No log	30.0	90	0.7213	0.4798	0.7202	0.8487
No log	31.0	93	0.7732	0.4478	0.7717	0.8785
No log	32.0	96	0.7161	0.5019	0.7151	0.8456
No log	33.0	99	0.6391	0.4956	0.6379	0.7987
No log	34.0	102	0.6728	0.5217	0.6716	0.8195
No log	35.0	105	0.6575	0.5259	0.6565	0.8103
No log	36.0	108	0.6125	0.5457	0.6115	0.7820
No log	37.0	111	0.6145	0.5461	0.6136	0.7833
No log	38.0	114	0.6072	0.5699	0.6060	0.7785
No log	39.0	117	0.6414	0.5593	0.6403	0.8002
No log	40.0	120	0.6415	0.5497	0.6404	0.8002
No log	41.0	123	0.6547	0.5432	0.6538	0.8086
No log	42.0	126	0.7015	0.4840	0.7003	0.8368
No log	43.0	129	0.5900	0.5445	0.5890	0.7675
No log	44.0	132	0.6155	0.5436	0.6145	0.7839
No log	45.0	135	0.6822	0.5234	0.6814	0.8255
No log	46.0	138	0.6871	0.5219	0.6864	0.8285
No log	47.0	141	0.6340	0.5455	0.6331	0.7956
No log	48.0	144	0.6894	0.5423	0.6888	0.8299
No log	49.0	147	0.5870	0.5771	0.5861	0.7656
No log	50.0	150	0.5807	0.5691	0.5798	0.7614
No log	51.0	153	0.5958	0.5821	0.5949	0.7713
No log	52.0	156	0.6102	0.5621	0.6093	0.7806
No log	53.0	159	0.6115	0.5373	0.6106	0.7814
No log	54.0	162	0.6223	0.5019	0.6213	0.7882
No log	55.0	165	0.6332	0.5317	0.6323	0.7952
No log	56.0	168	0.6321	0.5495	0.6311	0.7944
No log	57.0	171	0.6205	0.5474	0.6196	0.7871
No log	58.0	174	0.6170	0.5589	0.6159	0.7848
No log	59.0	177	0.5856	0.5484	0.5846	0.7646
No log	60.0	180	0.5971	0.5575	0.5962	0.7721
No log	61.0	183	0.6828	0.5120	0.6820	0.8258
No log	62.0	186	0.6766	0.4747	0.6755	0.8219
No log	63.0	189	0.6283	0.5333	0.6273	0.7920
No log	64.0	192	0.7197	0.5071	0.7190	0.8479
No log	65.0	195	0.6115	0.5428	0.6104	0.7813
No log	66.0	198	0.6037	0.5522	0.6026	0.7763
No log	67.0	201	0.6276	0.5424	0.6267	0.7916
No log	68.0	204	0.6348	0.5381	0.6336	0.7960
No log	69.0	207	0.6272	0.5359	0.6260	0.7912
No log	70.0	210	0.6301	0.5313	0.6292	0.7932
No log	71.0	213	0.6116	0.5315	0.6107	0.7815

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV16_k3_task1_organization_k3_k3_fold1

ASAP_FineTuningBERT_AugV16_k3_task1_organization_k3_k3_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV16_k3_task1_organization_k3_k3_fold1