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	2	11.0468	0.0023	11.0468	3.3237
No log	2.0	4	10.0769	0.0018	10.0769	3.1744
No log	3.0	6	8.7817	0.0	8.7817	2.9634
No log	4.0	8	7.6158	0.0	7.6158	2.7597
No log	5.0	10	6.0436	0.0033	6.0436	2.4584
No log	6.0	12	4.3252	0.0079	4.3252	2.0797
No log	7.0	14	3.1143	0.0040	3.1143	1.7647
No log	8.0	16	2.4279	0.0773	2.4279	1.5582
No log	9.0	18	1.8662	0.1276	1.8662	1.3661
No log	10.0	20	1.4175	0.0634	1.4175	1.1906
No log	11.0	22	1.2109	0.0945	1.2109	1.1004
No log	12.0	24	1.1817	0.1056	1.1817	1.0871
No log	13.0	26	0.8864	0.1273	0.8864	0.9415
No log	14.0	28	0.7253	0.4990	0.7253	0.8517
No log	15.0	30	0.8103	0.3896	0.8103	0.9002
No log	16.0	32	1.8174	0.2351	1.8174	1.3481
No log	17.0	34	1.9505	0.2230	1.9505	1.3966
No log	18.0	36	0.9247	0.3012	0.9247	0.9616
No log	19.0	38	0.6551	0.4026	0.6551	0.8094
No log	20.0	40	0.6782	0.3927	0.6782	0.8235
No log	21.0	42	0.8129	0.3344	0.8129	0.9016
No log	22.0	44	0.9354	0.3040	0.9354	0.9672
No log	23.0	46	0.9231	0.3775	0.9231	0.9608
No log	24.0	48	0.9526	0.3574	0.9526	0.9760
No log	25.0	50	1.0235	0.3987	1.0235	1.0117
No log	26.0	52	0.7951	0.5151	0.7951	0.8917
No log	27.0	54	0.5853	0.5376	0.5853	0.7651
No log	28.0	56	0.5876	0.5446	0.5876	0.7665
No log	29.0	58	0.6986	0.5325	0.6986	0.8359
No log	30.0	60	0.6828	0.5424	0.6828	0.8263
No log	31.0	62	0.6098	0.5481	0.6098	0.7809
No log	32.0	64	0.6142	0.5619	0.6142	0.7837
No log	33.0	66	0.6346	0.6019	0.6346	0.7966
No log	34.0	68	0.6613	0.5943	0.6613	0.8132
No log	35.0	70	0.6430	0.6103	0.6430	0.8019
No log	36.0	72	0.6586	0.5980	0.6586	0.8116
No log	37.0	74	0.6670	0.6092	0.6670	0.8167
No log	38.0	76	0.6930	0.6036	0.6930	0.8324
No log	39.0	78	0.7684	0.5856	0.7684	0.8766
No log	40.0	80	0.7216	0.6150	0.7216	0.8495
No log	41.0	82	0.7308	0.5929	0.7308	0.8549
No log	42.0	84	0.7869	0.5711	0.7869	0.8871
No log	43.0	86	0.7356	0.5905	0.7356	0.8577
No log	44.0	88	0.6643	0.5830	0.6643	0.8150
No log	45.0	90	0.6595	0.6237	0.6595	0.8121
No log	46.0	92	0.8265	0.5581	0.8265	0.9091
No log	47.0	94	0.6815	0.6280	0.6815	0.8255
No log	48.0	96	0.7080	0.5593	0.7080	0.8414
No log	49.0	98	0.6771	0.5773	0.6771	0.8228
No log	50.0	100	0.6470	0.6276	0.6470	0.8044
No log	51.0	102	0.7209	0.6000	0.7209	0.8490
No log	52.0	104	0.6977	0.5991	0.6977	0.8353
No log	53.0	106	0.6580	0.6137	0.6580	0.8112
No log	54.0	108	0.7084	0.6310	0.7084	0.8417
No log	55.0	110	0.7921	0.6088	0.7921	0.8900
No log	56.0	112	0.7220	0.6462	0.7220	0.8497
No log	57.0	114	0.7163	0.6296	0.7163	0.8463
No log	58.0	116	0.7903	0.5958	0.7903	0.8890
No log	59.0	118	0.7478	0.5757	0.7478	0.8648
No log	60.0	120	0.8426	0.5597	0.8426	0.9180
No log	61.0	122	0.7722	0.5719	0.7722	0.8787
No log	62.0	124	0.7789	0.5784	0.7789	0.8826
No log	63.0	126	0.7584	0.6041	0.7584	0.8708
No log	64.0	128	0.7516	0.6046	0.7516	0.8669
No log	65.0	130	0.6870	0.6232	0.6870	0.8289
No log	66.0	132	0.6922	0.6094	0.6922	0.8320
No log	67.0	134	0.7302	0.5970	0.7302	0.8545
No log	68.0	136	0.7153	0.6176	0.7153	0.8457
No log	69.0	138	0.7173	0.6316	0.7173	0.8469
No log	70.0	140	0.6908	0.6411	0.6908	0.8311
No log	71.0	142	0.7069	0.6422	0.7069	0.8408
No log	72.0	144	0.6665	0.6533	0.6665	0.8164
No log	73.0	146	0.6685	0.6351	0.6685	0.8176
No log	74.0	148	0.7547	0.5916	0.7547	0.8687
No log	75.0	150	0.7626	0.5832	0.7626	0.8733
No log	76.0	152	0.6653	0.6250	0.6653	0.8156
No log	77.0	154	0.6572	0.6218	0.6572	0.8107
No log	78.0	156	0.6803	0.6237	0.6803	0.8248
No log	79.0	158	0.7561	0.5944	0.7561	0.8695
No log	80.0	160	0.7488	0.5961	0.7488	0.8654
No log	81.0	162	0.6953	0.6192	0.6953	0.8338
No log	82.0	164	0.6844	0.6211	0.6844	0.8273
No log	83.0	166	0.6948	0.6306	0.6948	0.8335
No log	84.0	168	0.7344	0.6120	0.7344	0.8569
No log	85.0	170	0.7030	0.6199	0.7030	0.8385
No log	86.0	172	0.6601	0.6216	0.6601	0.8125
No log	87.0	174	0.6485	0.6245	0.6485	0.8053
No log	88.0	176	0.6573	0.6203	0.6573	0.8108
No log	89.0	178	0.6970	0.6223	0.6970	0.8349
No log	90.0	180	0.7019	0.6173	0.7019	0.8378
No log	91.0	182	0.6966	0.6214	0.6966	0.8346
No log	92.0	184	0.6874	0.6160	0.6874	0.8291

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold4

ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold4