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	1	12.3790	-0.0050	12.3790	3.5184
No log	2.0	2	11.2683	-0.0238	11.2683	3.3568
No log	3.0	3	10.0365	-0.0001	10.0365	3.1680
No log	4.0	4	8.9606	0.0018	8.9606	2.9934
No log	5.0	5	8.1050	0.0018	8.1050	2.8469
No log	6.0	6	7.2370	0.0018	7.2370	2.6902
No log	7.0	7	6.4863	0.0018	6.4863	2.5468
No log	8.0	8	6.0292	0.0017	6.0292	2.4554
No log	9.0	9	5.3678	0.0129	5.3678	2.3169
No log	10.0	10	4.6314	0.0128	4.6314	2.1521
No log	11.0	11	4.1469	0.0079	4.1469	2.0364
No log	12.0	12	3.7013	0.0040	3.7013	1.9239
No log	13.0	13	3.2735	0.0040	3.2735	1.8093
No log	14.0	14	3.0046	0.0040	3.0046	1.7334
No log	15.0	15	2.6444	0.0040	2.6444	1.6262
No log	16.0	16	2.3126	0.1537	2.3126	1.5207
No log	17.0	17	2.0898	0.1105	2.0898	1.4456
No log	18.0	18	1.8939	0.0509	1.8939	1.3762
No log	19.0	19	1.7042	0.0445	1.7042	1.3054
No log	20.0	20	1.5388	0.0420	1.5388	1.2405
No log	21.0	21	1.3906	0.0316	1.3906	1.1792
No log	22.0	22	1.2591	0.0316	1.2591	1.1221
No log	23.0	23	1.1541	0.0316	1.1541	1.0743
No log	24.0	24	1.0587	0.0316	1.0587	1.0289
No log	25.0	25	0.9774	0.0316	0.9774	0.9886
No log	26.0	26	0.9223	0.0316	0.9223	0.9604
No log	27.0	27	0.8539	0.1538	0.8539	0.9241
No log	28.0	28	0.7997	0.3959	0.7997	0.8943
No log	29.0	29	0.7483	0.4590	0.7483	0.8650
No log	30.0	30	0.7024	0.4608	0.7024	0.8381
No log	31.0	31	0.6603	0.4780	0.6603	0.8126
No log	32.0	32	0.6226	0.4821	0.6226	0.7890
No log	33.0	33	0.5920	0.4760	0.5920	0.7694
No log	34.0	34	0.5622	0.4872	0.5622	0.7498
No log	35.0	35	0.5392	0.5099	0.5392	0.7343
No log	36.0	36	0.5221	0.5646	0.5221	0.7226
No log	37.0	37	0.5036	0.5440	0.5036	0.7097
No log	38.0	38	0.5360	0.4980	0.5360	0.7322
No log	39.0	39	0.5208	0.5155	0.5208	0.7217
No log	40.0	40	0.4930	0.5591	0.4930	0.7021
No log	41.0	41	0.4921	0.5573	0.4921	0.7015
No log	42.0	42	0.5424	0.5166	0.5424	0.7365
No log	43.0	43	0.5626	0.5772	0.5626	0.7501
No log	44.0	44	0.5623	0.6103	0.5623	0.7499
No log	45.0	45	0.5471	0.6165	0.5471	0.7396
No log	46.0	46	0.5379	0.6304	0.5379	0.7334
No log	47.0	47	0.6413	0.5888	0.6413	0.8008
No log	48.0	48	0.6501	0.5948	0.6501	0.8063
No log	49.0	49	0.5829	0.6244	0.5829	0.7635
No log	50.0	50	0.6381	0.5998	0.6381	0.7988
No log	51.0	51	0.8007	0.5463	0.8007	0.8948
No log	52.0	52	0.7838	0.5557	0.7838	0.8853
No log	53.0	53	0.6527	0.6069	0.6527	0.8079
No log	54.0	54	0.6304	0.6201	0.6304	0.7939
No log	55.0	55	0.7289	0.5754	0.7289	0.8538
No log	56.0	56	0.8423	0.5425	0.8423	0.9178
No log	57.0	57	0.8083	0.5556	0.8083	0.8991
No log	58.0	58	0.7065	0.5933	0.7065	0.8405
No log	59.0	59	0.7515	0.5819	0.7515	0.8669
No log	60.0	60	0.7928	0.5732	0.7928	0.8904
No log	61.0	61	0.9809	0.5265	0.9809	0.9904
No log	62.0	62	1.0644	0.5191	1.0644	1.0317
No log	63.0	63	0.9846	0.5418	0.9846	0.9923
No log	64.0	64	1.0122	0.5418	1.0122	1.0061
No log	65.0	65	0.8940	0.5732	0.8940	0.9455
No log	66.0	66	0.9060	0.5629	0.9060	0.9518

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4

Version3ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version3ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4