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	11.0305	-0.0105	11.0305	3.3212
No log	2.0	4	9.4602	0.0018	9.4602	3.0757
No log	3.0	6	8.0308	0.0	8.0308	2.8339
No log	4.0	8	6.4606	0.0	6.4606	2.5418
No log	5.0	10	5.1631	0.0152	5.1631	2.2722
No log	6.0	12	4.2664	0.0040	4.2664	2.0655
No log	7.0	14	3.4918	0.0040	3.4918	1.8686
No log	8.0	16	2.7313	0.0040	2.7313	1.6527
No log	9.0	18	2.1579	0.1012	2.1579	1.4690
No log	10.0	20	1.7465	0.0317	1.7465	1.3215
No log	11.0	22	1.5345	0.0107	1.5345	1.2388
No log	12.0	24	1.3257	0.0107	1.3257	1.1514
No log	13.0	26	1.1451	0.0107	1.1451	1.0701
No log	14.0	28	1.0761	0.0107	1.0761	1.0373
No log	15.0	30	1.0079	0.0107	1.0079	1.0039
No log	16.0	32	0.9558	0.0236	0.9558	0.9776
No log	17.0	34	0.8649	0.1903	0.8649	0.9300
No log	18.0	36	0.7829	0.3505	0.7829	0.8848
No log	19.0	38	0.7777	0.3834	0.7777	0.8819
No log	20.0	40	0.7053	0.4695	0.7053	0.8398
No log	21.0	42	0.6479	0.4746	0.6479	0.8049
No log	22.0	44	0.6627	0.4455	0.6627	0.8141
No log	23.0	46	0.7951	0.4328	0.7951	0.8917
No log	24.0	48	0.6641	0.4646	0.6641	0.8149
No log	25.0	50	0.6391	0.5049	0.6391	0.7995
No log	26.0	52	0.6577	0.4868	0.6577	0.8110
No log	27.0	54	0.8827	0.4675	0.8827	0.9395
No log	28.0	56	0.7547	0.5213	0.7547	0.8688
No log	29.0	58	0.6910	0.5605	0.6910	0.8313
No log	30.0	60	0.7276	0.5444	0.7276	0.8530
No log	31.0	62	0.8309	0.5199	0.8309	0.9115
No log	32.0	64	0.7507	0.5479	0.7507	0.8665
No log	33.0	66	0.7686	0.5418	0.7686	0.8767
No log	34.0	68	0.7717	0.5377	0.7717	0.8785
No log	35.0	70	0.8316	0.5053	0.8316	0.9119
No log	36.0	72	0.7859	0.5649	0.7859	0.8865
No log	37.0	74	0.8452	0.5646	0.8452	0.9194
No log	38.0	76	0.7929	0.5892	0.7929	0.8904
No log	39.0	78	0.9602	0.5315	0.9602	0.9799
No log	40.0	80	0.9890	0.5064	0.9890	0.9945
No log	41.0	82	0.7327	0.5964	0.7327	0.8560
No log	42.0	84	0.7937	0.5703	0.7937	0.8909
No log	43.0	86	0.7815	0.5745	0.7815	0.8840
No log	44.0	88	0.7461	0.5561	0.7461	0.8638
No log	45.0	90	0.7821	0.5680	0.7821	0.8844
No log	46.0	92	0.7608	0.5726	0.7608	0.8723
No log	47.0	94	0.7556	0.5659	0.7556	0.8693
No log	48.0	96	0.7777	0.5740	0.7777	0.8819
No log	49.0	98	0.7698	0.5668	0.7698	0.8774
No log	50.0	100	0.7762	0.5328	0.7762	0.8810
No log	51.0	102	0.7670	0.5222	0.7670	0.8758
No log	52.0	104	0.7632	0.5631	0.7632	0.8736
No log	53.0	106	0.7730	0.5571	0.7730	0.8792
No log	54.0	108	0.8266	0.5593	0.8266	0.9092
No log	55.0	110	0.8579	0.5532	0.8579	0.9263
No log	56.0	112	0.8562	0.5597	0.8562	0.9253
No log	57.0	114	0.8115	0.5628	0.8115	0.9008
No log	58.0	116	0.8087	0.5684	0.8087	0.8993
No log	59.0	118	0.7763	0.5698	0.7763	0.8811
No log	60.0	120	0.7371	0.5640	0.7371	0.8585
No log	61.0	122	0.8337	0.5677	0.8337	0.9131

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV9_k1_task1_organization_k1_k1_fold4

ASAP_FineTuningBERT_AugV9_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_AugV9_k1_task1_organization_k1_k1_fold4