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	8	4.7000	0.0140	4.6990	2.1677
No log	2.0	16	1.5057	0.0365	1.5049	1.2268
No log	3.0	24	1.8683	0.0486	1.8674	1.3665
No log	4.0	32	1.5173	0.0549	1.5167	1.2316
No log	5.0	40	1.0125	0.2435	1.0123	1.0061
No log	6.0	48	1.0389	0.2528	1.0392	1.0194
No log	7.0	56	0.7990	0.3808	0.7994	0.8941
No log	8.0	64	0.7491	0.4412	0.7496	0.8658
No log	9.0	72	0.8099	0.3925	0.8106	0.9003
No log	10.0	80	0.8424	0.3872	0.8430	0.9182
No log	11.0	88	0.8855	0.3992	0.8862	0.9414
No log	12.0	96	0.8510	0.3756	0.8515	0.9227
No log	13.0	104	0.8177	0.4212	0.8182	0.9045
No log	14.0	112	0.9127	0.3870	0.9132	0.9556
No log	15.0	120	0.8366	0.4494	0.8371	0.9149
No log	16.0	128	0.8642	0.4154	0.8647	0.9299
No log	17.0	136	0.8820	0.4143	0.8826	0.9394
No log	18.0	144	0.8479	0.4089	0.8486	0.9212
No log	19.0	152	0.7493	0.4776	0.7500	0.8660
No log	20.0	160	0.9268	0.3675	0.9272	0.9629
No log	21.0	168	1.3255	0.2585	1.3259	1.1515
No log	22.0	176	0.9079	0.3788	0.9082	0.9530
No log	23.0	184	0.8822	0.3771	0.8827	0.9395
No log	24.0	192	0.7369	0.4491	0.7374	0.8587
No log	25.0	200	0.8312	0.4147	0.8317	0.9120
No log	26.0	208	0.8334	0.3961	0.8337	0.9131
No log	27.0	216	0.7532	0.4265	0.7536	0.8681
No log	28.0	224	0.8270	0.3946	0.8273	0.9095
No log	29.0	232	0.9336	0.3586	0.9339	0.9664
No log	30.0	240	0.9960	0.3551	0.9964	0.9982
No log	31.0	248	0.9875	0.3602	0.9879	0.9939
No log	32.0	256	1.0056	0.3429	1.0059	1.0030
No log	33.0	264	0.9875	0.3536	0.9878	0.9939
No log	34.0	272	0.7810	0.4425	0.7814	0.8840
No log	35.0	280	0.6962	0.4532	0.6964	0.8345
No log	36.0	288	0.7702	0.4038	0.7704	0.8777
No log	37.0	296	0.6532	0.4911	0.6534	0.8084
No log	38.0	304	0.7096	0.4735	0.7097	0.8424
No log	39.0	312	0.7285	0.4421	0.7287	0.8536
No log	40.0	320	0.6993	0.4575	0.6994	0.8363
No log	41.0	328	0.7831	0.4053	0.7832	0.8850
No log	42.0	336	0.7305	0.4344	0.7307	0.8548
No log	43.0	344	0.7438	0.4255	0.7440	0.8626
No log	44.0	352	0.7279	0.4388	0.7280	0.8532
No log	45.0	360	0.6792	0.4501	0.6794	0.8243
No log	46.0	368	0.7094	0.4425	0.7096	0.8424
No log	47.0	376	0.6721	0.4670	0.6723	0.8199
No log	48.0	384	0.7257	0.4163	0.7259	0.8520
No log	49.0	392	0.7402	0.4215	0.7404	0.8605
No log	50.0	400	0.8054	0.3778	0.8055	0.8975
No log	51.0	408	0.7239	0.4385	0.7241	0.8510
No log	52.0	416	0.7307	0.4139	0.7309	0.8549
No log	53.0	424	0.7868	0.3707	0.7870	0.8871
No log	54.0	432	0.7107	0.4587	0.7108	0.8431
No log	55.0	440	0.7694	0.4197	0.7696	0.8772
No log	56.0	448	0.7343	0.4249	0.7344	0.8570
No log	57.0	456	0.7248	0.4477	0.7250	0.8514

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV6_k10_task1_organization_k10_k10_fold3

ASAP_FineTuningBERT_AugV6_k10_task1_organization_k10_k10_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV6_k10_task1_organization_k10_k10_fold3