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	13.2505	-0.0020	13.2505	3.6401
No log	2.0	4	11.1619	0.0126	11.1619	3.3409
No log	3.0	6	7.1718	0.0116	7.1718	2.6780
No log	4.0	8	7.0889	-0.0096	7.0889	2.6625
No log	5.0	10	4.0501	0.0250	4.0501	2.0125
No log	6.0	12	4.5200	0.0267	4.5200	2.1260
No log	7.0	14	2.8302	0.0511	2.8302	1.6823
No log	8.0	16	2.3180	0.0712	2.3180	1.5225
No log	9.0	18	1.6162	0.1117	1.6162	1.2713
No log	10.0	20	1.2355	0.0839	1.2355	1.1115
No log	11.0	22	1.5046	0.1192	1.5046	1.2266
No log	12.0	24	1.2349	0.0840	1.2349	1.1113
No log	13.0	26	0.8379	0.2003	0.8379	0.9154
No log	14.0	28	0.7571	0.3501	0.7571	0.8701
No log	15.0	30	1.5341	0.1052	1.5341	1.2386
No log	16.0	32	1.5508	0.1119	1.5508	1.2453
No log	17.0	34	0.8673	0.2921	0.8673	0.9313
No log	18.0	36	0.7623	0.2538	0.7623	0.8731
No log	19.0	38	0.7381	0.3735	0.7381	0.8591
No log	20.0	40	1.2266	0.2553	1.2266	1.1075
No log	21.0	42	1.1107	0.2826	1.1107	1.0539
No log	22.0	44	0.6899	0.3756	0.6899	0.8306
No log	23.0	46	0.7314	0.3400	0.7314	0.8552
No log	24.0	48	0.6701	0.3967	0.6701	0.8186
No log	25.0	50	0.9750	0.2784	0.9750	0.9874
No log	26.0	52	0.8198	0.3523	0.8198	0.9054
No log	27.0	54	0.7022	0.4061	0.7022	0.8380
No log	28.0	56	0.7363	0.3940	0.7363	0.8581
No log	29.0	58	0.7997	0.4107	0.7997	0.8943
No log	30.0	60	0.8612	0.3992	0.8612	0.9280
No log	31.0	62	0.7596	0.4342	0.7596	0.8716
No log	32.0	64	0.7922	0.4335	0.7922	0.8901
No log	33.0	66	1.0637	0.4019	1.0637	1.0314
No log	34.0	68	0.8860	0.4198	0.8860	0.9413
No log	35.0	70	0.7734	0.4712	0.7734	0.8795
No log	36.0	72	0.7560	0.4807	0.7560	0.8695
No log	37.0	74	0.9703	0.4036	0.9703	0.9850
No log	38.0	76	0.8442	0.4495	0.8442	0.9188
No log	39.0	78	0.9032	0.4754	0.9032	0.9504
No log	40.0	80	0.8802	0.4764	0.8802	0.9382
No log	41.0	82	0.8974	0.4556	0.8974	0.9473
No log	42.0	84	0.8159	0.4763	0.8159	0.9033
No log	43.0	86	0.9549	0.4883	0.9549	0.9772
No log	44.0	88	0.9616	0.4814	0.9616	0.9806
No log	45.0	90	0.7303	0.5201	0.7303	0.8546
No log	46.0	92	0.7348	0.5135	0.7348	0.8572
No log	47.0	94	0.7544	0.5175	0.7544	0.8686
No log	48.0	96	0.8406	0.4976	0.8406	0.9169
No log	49.0	98	0.7668	0.4964	0.7668	0.8757
No log	50.0	100	0.7754	0.5022	0.7754	0.8806
No log	51.0	102	0.7810	0.4938	0.7810	0.8837
No log	52.0	104	0.7874	0.5017	0.7874	0.8874
No log	53.0	106	0.7835	0.5008	0.7835	0.8851
No log	54.0	108	0.7964	0.4968	0.7964	0.8924
No log	55.0	110	0.7470	0.5086	0.7470	0.8643
No log	56.0	112	0.7681	0.5103	0.7681	0.8764
No log	57.0	114	0.8775	0.5054	0.8775	0.9368
No log	58.0	116	0.9120	0.4937	0.9120	0.9550
No log	59.0	118	0.7486	0.5047	0.7486	0.8652
No log	60.0	120	0.6898	0.5337	0.6898	0.8305
No log	61.0	122	0.6756	0.5226	0.6756	0.8219
No log	62.0	124	0.9200	0.5089	0.9200	0.9591
No log	63.0	126	1.0391	0.4849	1.0391	1.0194
No log	64.0	128	0.8159	0.5186	0.8159	0.9033
No log	65.0	130	0.7299	0.4843	0.7299	0.8544
No log	66.0	132	0.7231	0.4945	0.7231	0.8504
No log	67.0	134	0.7021	0.5384	0.7021	0.8379
No log	68.0	136	0.7085	0.5473	0.7085	0.8417
No log	69.0	138	0.6906	0.5322	0.6906	0.8310
No log	70.0	140	0.6987	0.5379	0.6987	0.8359
No log	71.0	142	0.6900	0.5335	0.6900	0.8307
No log	72.0	144	0.7159	0.5494	0.7159	0.8461
No log	73.0	146	0.7499	0.5439	0.7499	0.8660
No log	74.0	148	0.7415	0.5486	0.7415	0.8611
No log	75.0	150	0.6758	0.5424	0.6758	0.8221
No log	76.0	152	0.6601	0.5219	0.6601	0.8125
No log	77.0	154	0.6569	0.5192	0.6569	0.8105
No log	78.0	156	0.6807	0.5322	0.6807	0.8250
No log	79.0	158	0.6761	0.5427	0.6761	0.8223
No log	80.0	160	0.6523	0.5325	0.6523	0.8077
No log	81.0	162	0.6401	0.5134	0.6401	0.8000
No log	82.0	164	0.6274	0.5253	0.6274	0.7921
No log	83.0	166	0.6111	0.5272	0.6111	0.7817
No log	84.0	168	0.6066	0.5197	0.6066	0.7789
No log	85.0	170	0.6097	0.5374	0.6097	0.7808
No log	86.0	172	0.6117	0.5399	0.6117	0.7821
No log	87.0	174	0.6230	0.5267	0.6230	0.7893
No log	88.0	176	0.6374	0.5273	0.6374	0.7984
No log	89.0	178	0.6522	0.5346	0.6522	0.8076
No log	90.0	180	0.6663	0.5350	0.6663	0.8163
No log	91.0	182	0.6779	0.5347	0.6779	0.8234
No log	92.0	184	0.6828	0.5362	0.6828	0.8263

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_changed_semanticV2_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0