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	4	10.0644	0.0018	10.0644	3.1724
No log	2.0	8	8.0368	0.0	8.0368	2.8349
No log	3.0	12	7.6353	0.0	7.6353	2.7632
No log	4.0	16	7.2247	0.0	7.2247	2.6879
No log	5.0	20	6.6603	0.0	6.6603	2.5808
No log	6.0	24	5.3534	0.0040	5.3534	2.3137
No log	7.0	28	3.3965	0.0040	3.3965	1.8430
No log	8.0	32	2.0022	0.0973	2.0022	1.4150
No log	9.0	36	1.2052	0.0213	1.2052	1.0978
No log	10.0	40	0.8218	0.2567	0.8218	0.9065
No log	11.0	44	0.7903	0.3252	0.7903	0.8890
No log	12.0	48	0.7685	0.4160	0.7685	0.8766
No log	13.0	52	0.6736	0.4334	0.6736	0.8207
No log	14.0	56	0.6152	0.4788	0.6152	0.7844
No log	15.0	60	1.0581	0.4049	1.0581	1.0286
No log	16.0	64	0.5888	0.4607	0.5888	0.7673
No log	17.0	68	0.7883	0.4233	0.7883	0.8879
No log	18.0	72	0.6455	0.4739	0.6455	0.8035
No log	19.0	76	0.6004	0.5081	0.6004	0.7749
No log	20.0	80	0.5746	0.5778	0.5746	0.7580
No log	21.0	84	0.6494	0.5735	0.6494	0.8058
No log	22.0	88	0.7003	0.5628	0.7003	0.8368
No log	23.0	92	0.6792	0.5743	0.6792	0.8241
No log	24.0	96	0.6904	0.5674	0.6904	0.8309
No log	25.0	100	0.6932	0.5739	0.6932	0.8326
No log	26.0	104	0.7305	0.5703	0.7305	0.8547
No log	27.0	108	0.7573	0.5636	0.7573	0.8702
No log	28.0	112	0.7593	0.5529	0.7593	0.8714
No log	29.0	116	0.6154	0.6202	0.6154	0.7845
No log	30.0	120	0.6323	0.5931	0.6323	0.7952
No log	31.0	124	0.7351	0.5650	0.7351	0.8574
No log	32.0	128	0.6901	0.5802	0.6901	0.8307
No log	33.0	132	0.8288	0.5618	0.8288	0.9104
No log	34.0	136	0.8248	0.5547	0.8248	0.9082
No log	35.0	140	0.6249	0.6002	0.6249	0.7905
No log	36.0	144	0.9170	0.5132	0.9170	0.9576
No log	37.0	148	0.6045	0.6360	0.6045	0.7775
No log	38.0	152	0.8782	0.5291	0.8782	0.9371
No log	39.0	156	0.6856	0.6140	0.6856	0.8280
No log	40.0	160	0.7106	0.6059	0.7106	0.8430
No log	41.0	164	0.6271	0.6090	0.6271	0.7919
No log	42.0	168	0.5841	0.5922	0.5841	0.7643
No log	43.0	172	0.8074	0.5268	0.8074	0.8985
No log	44.0	176	0.6030	0.6024	0.6030	0.7765
No log	45.0	180	0.8320	0.5313	0.8320	0.9121
No log	46.0	184	0.7422	0.5754	0.7422	0.8615
No log	47.0	188	0.7847	0.5676	0.7847	0.8858
No log	48.0	192	0.5970	0.6334	0.5970	0.7727
No log	49.0	196	0.6962	0.5957	0.6962	0.8344
No log	50.0	200	0.6347	0.6281	0.6347	0.7967
No log	51.0	204	0.7899	0.5823	0.7899	0.8888
No log	52.0	208	0.6429	0.6278	0.6429	0.8018
No log	53.0	212	0.8402	0.5656	0.8402	0.9166
No log	54.0	216	0.5935	0.6559	0.5935	0.7704
No log	55.0	220	0.6559	0.5988	0.6559	0.8099
No log	56.0	224	0.6387	0.6034	0.6387	0.7992
No log	57.0	228	0.6613	0.6100	0.6613	0.8132
No log	58.0	232	0.7342	0.5916	0.7342	0.8569
No log	59.0	236	0.6262	0.6163	0.6262	0.7913
No log	60.0	240	0.6698	0.5981	0.6698	0.8184
No log	61.0	244	0.6870	0.5900	0.6870	0.8289
No log	62.0	248	0.6366	0.5975	0.6366	0.7979
No log	63.0	252	0.6508	0.6027	0.6508	0.8067
No log	64.0	256	0.6824	0.6078	0.6824	0.8261
No log	65.0	260	0.7456	0.5968	0.7456	0.8635
No log	66.0	264	0.6987	0.5965	0.6987	0.8359
No log	67.0	268	0.6811	0.5928	0.6811	0.8253
No log	68.0	272	0.6882	0.5915	0.6882	0.8296
No log	69.0	276	0.6921	0.5938	0.6921	0.8319
No log	70.0	280	0.7034	0.5988	0.7034	0.8387
No log	71.0	284	0.6984	0.6009	0.6984	0.8357
No log	72.0	288	0.6759	0.6020	0.6759	0.8221
No log	73.0	292	0.6628	0.5980	0.6628	0.8141
No log	74.0	296	0.7111	0.6048	0.7111	0.8433

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV16_k5_task1_organization_k5_k5_fold4

ASAP_FineTuningBERT_AugV16_k5_task1_organization_k5_k5_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV16_k5_task1_organization_k5_k5_fold4