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	10.5247	0.0055	10.5247	3.2442
No log	2.0	6	8.4201	0.0037	8.4201	2.9017
No log	3.0	9	6.8615	0.0018	6.8615	2.6195
No log	4.0	12	5.3684	0.0502	5.3684	2.3170
No log	5.0	15	4.2092	0.0118	4.2092	2.0516
No log	6.0	18	3.2139	0.0079	3.2139	1.7927
No log	7.0	21	2.4597	0.0094	2.4597	1.5683
No log	8.0	24	1.9035	0.0855	1.9035	1.3797
No log	9.0	27	1.4379	0.0316	1.4379	1.1991
No log	10.0	30	1.2122	0.0937	1.2122	1.1010
No log	11.0	33	1.4645	0.2032	1.4645	1.2102
No log	12.0	36	1.5633	0.2222	1.5633	1.2503
No log	13.0	39	1.3647	0.2088	1.3647	1.1682
No log	14.0	42	1.2320	0.2234	1.2320	1.1099
No log	15.0	45	1.0106	0.3241	1.0106	1.0053
No log	16.0	48	1.7406	0.2136	1.7406	1.3193
No log	17.0	51	0.6997	0.4745	0.6997	0.8365
No log	18.0	54	1.1111	0.3422	1.1111	1.0541
No log	19.0	57	0.8900	0.4048	0.8900	0.9434
No log	20.0	60	0.6746	0.4533	0.6746	0.8214
No log	21.0	63	0.9945	0.4235	0.9945	0.9972
No log	22.0	66	0.7740	0.4651	0.7740	0.8798
No log	23.0	69	1.1148	0.4262	1.1148	1.0558
No log	24.0	72	0.7845	0.4719	0.7845	0.8857
No log	25.0	75	1.2113	0.4172	1.2113	1.1006
No log	26.0	78	0.8998	0.4621	0.8998	0.9486
No log	27.0	81	1.2122	0.4200	1.2122	1.1010
No log	28.0	84	1.3059	0.4156	1.3059	1.1427
No log	29.0	87	0.9490	0.4595	0.9490	0.9742
No log	30.0	90	1.0540	0.4390	1.0540	1.0266
No log	31.0	93	0.8932	0.4305	0.8932	0.9451
No log	32.0	96	1.0519	0.3881	1.0519	1.0256
No log	33.0	99	0.8158	0.4532	0.8158	0.9032
No log	34.0	102	0.9152	0.4655	0.9152	0.9567
No log	35.0	105	0.9016	0.4864	0.9016	0.9495
No log	36.0	108	0.8656	0.5086	0.8656	0.9304
No log	37.0	111	0.9171	0.4823	0.9171	0.9576
No log	38.0	114	0.8492	0.5189	0.8492	0.9215
No log	39.0	117	0.9708	0.4717	0.9708	0.9853
No log	40.0	120	0.7942	0.5182	0.7942	0.8912
No log	41.0	123	0.8061	0.5136	0.8061	0.8978
No log	42.0	126	1.2008	0.4035	1.2008	1.0958
No log	43.0	129	0.7336	0.5069	0.7336	0.8565
No log	44.0	132	0.8419	0.4899	0.8419	0.9176
No log	45.0	135	0.8029	0.5178	0.8029	0.8961
No log	46.0	138	0.8238	0.5267	0.8238	0.9076
No log	47.0	141	0.7698	0.5201	0.7698	0.8774
No log	48.0	144	0.7976	0.5026	0.7976	0.8931
No log	49.0	147	0.8330	0.5217	0.8330	0.9127
No log	50.0	150	0.8927	0.5193	0.8927	0.9448
No log	51.0	153	0.9009	0.5160	0.9009	0.9492
No log	52.0	156	0.8596	0.5308	0.8596	0.9271
No log	53.0	159	0.8258	0.5293	0.8258	0.9087
No log	54.0	162	0.8385	0.5276	0.8385	0.9157
No log	55.0	165	0.9055	0.5108	0.9055	0.9516
No log	56.0	168	0.8937	0.5259	0.8937	0.9454
No log	57.0	171	0.9121	0.5114	0.9121	0.9550
No log	58.0	174	0.8489	0.5254	0.8489	0.9213
No log	59.0	177	0.8823	0.5032	0.8823	0.9393
No log	60.0	180	0.8371	0.5255	0.8371	0.9149
No log	61.0	183	0.8983	0.5255	0.8983	0.9478
No log	62.0	186	0.8380	0.5321	0.8380	0.9154
No log	63.0	189	0.8237	0.5467	0.8237	0.9076
No log	64.0	192	0.9096	0.4976	0.9096	0.9537
No log	65.0	195	0.7689	0.5375	0.7689	0.8769
No log	66.0	198	0.8327	0.5310	0.8327	0.9125
No log	67.0	201	0.8032	0.5430	0.8032	0.8962
No log	68.0	204	0.8283	0.5465	0.8283	0.9101
No log	69.0	207	0.8521	0.5493	0.8521	0.9231
No log	70.0	210	0.8546	0.5216	0.8546	0.9244
No log	71.0	213	0.8397	0.5353	0.8397	0.9164
No log	72.0	216	0.8910	0.5116	0.8910	0.9439
No log	73.0	219	0.8442	0.5291	0.8442	0.9188
No log	74.0	222	0.7961	0.5385	0.7961	0.8922
No log	75.0	225	0.8270	0.5315	0.8270	0.9094
No log	76.0	228	0.8139	0.5353	0.8139	0.9022
No log	77.0	231	0.8262	0.5305	0.8262	0.9090
No log	78.0	234	0.8218	0.5301	0.8218	0.9066
No log	79.0	237	0.8256	0.5464	0.8256	0.9086
No log	80.0	240	0.8134	0.5491	0.8134	0.9019
No log	81.0	243	0.8102	0.5510	0.8102	0.9001
No log	82.0	246	0.8308	0.5286	0.8308	0.9115
No log	83.0	249	0.8132	0.5504	0.8132	0.9018
No log	84.0	252	0.8160	0.5506	0.8160	0.9033
No log	85.0	255	0.8275	0.5472	0.8275	0.9097
No log	86.0	258	0.8107	0.5355	0.8107	0.9004
No log	87.0	261	0.8180	0.5371	0.8180	0.9044
No log	88.0	264	0.8277	0.5459	0.8277	0.9098
No log	89.0	267	0.8325	0.5410	0.8325	0.9124
No log	90.0	270	0.8393	0.5488	0.8393	0.9161
No log	91.0	273	0.8415	0.5493	0.8415	0.9173
No log	92.0	276	0.8309	0.5465	0.8309	0.9116
No log	93.0	279	0.8184	0.5518	0.8184	0.9046
No log	94.0	282	0.8143	0.5511	0.8143	0.9024
No log	95.0	285	0.8105	0.5511	0.8105	0.9003
No log	96.0	288	0.8141	0.5491	0.8141	0.9023
No log	97.0	291	0.8113	0.5540	0.8113	0.9007
No log	98.0	294	0.8154	0.5527	0.8154	0.9030
No log	99.0	297	0.8158	0.5538	0.8158	0.9032
No log	100.0	300	0.8170	0.5497	0.8170	0.9039

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold4

Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version22NewTestASAP_FineTuningBERT_AugV22_k5_task1_organization_k5_k5_fold4