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	3	8.2410	0.0	8.2412	2.8707
No log	2.0	6	6.2586	0.0002	6.2588	2.5018
No log	3.0	9	4.5957	0.0025	4.5961	2.1439
No log	4.0	12	3.0275	0.0	3.0279	1.7401
No log	5.0	15	1.9173	0.0948	1.9177	1.3848
No log	6.0	18	1.2906	0.0241	1.2911	1.1362
No log	7.0	21	0.9672	0.0068	0.9676	0.9837
No log	8.0	24	0.8185	0.3219	0.8188	0.9049
No log	9.0	27	0.6879	0.4196	0.6880	0.8295
No log	10.0	30	0.8236	0.4591	0.8238	0.9076
No log	11.0	33	1.2058	0.3054	1.2061	1.0982
No log	12.0	36	0.6621	0.4553	0.6621	0.8137
No log	13.0	39	0.9232	0.3493	0.9235	0.9610
No log	14.0	42	0.6257	0.4378	0.6258	0.7911
No log	15.0	45	0.5950	0.4025	0.5950	0.7714
No log	16.0	48	0.6429	0.4529	0.6430	0.8019
No log	17.0	51	0.6441	0.4445	0.6442	0.8026
No log	18.0	54	0.6410	0.4434	0.6412	0.8008
No log	19.0	57	0.6047	0.4430	0.6047	0.7776
No log	20.0	60	1.0858	0.3929	1.0862	1.0422
No log	21.0	63	0.6177	0.4903	0.6176	0.7859
No log	22.0	66	0.6175	0.4724	0.6174	0.7857
No log	23.0	69	0.9839	0.4222	0.9840	0.9920
No log	24.0	72	0.6455	0.4971	0.6454	0.8034
No log	25.0	75	0.8263	0.4740	0.8264	0.9091
No log	26.0	78	0.6948	0.5152	0.6949	0.8336
No log	27.0	81	0.6494	0.5321	0.6493	0.8058
No log	28.0	84	1.2253	0.3685	1.2256	1.1071
No log	29.0	87	0.7155	0.5051	0.7156	0.8459
No log	30.0	90	0.6867	0.5155	0.6868	0.8287
No log	31.0	93	0.8013	0.4784	0.8016	0.8953
No log	32.0	96	0.7017	0.5198	0.7016	0.8376
No log	33.0	99	0.7062	0.4884	0.7065	0.8405
No log	34.0	102	0.6685	0.4985	0.6687	0.8177
No log	35.0	105	0.6889	0.5076	0.6890	0.8301
No log	36.0	108	0.6868	0.5159	0.6870	0.8289
No log	37.0	111	0.6852	0.5231	0.6854	0.8279
No log	38.0	114	0.7197	0.5259	0.7200	0.8485
No log	39.0	117	0.7286	0.5171	0.7289	0.8538
No log	40.0	120	0.7057	0.5428	0.7059	0.8402
No log	41.0	123	0.6852	0.5293	0.6854	0.8279
No log	42.0	126	0.6872	0.5332	0.6873	0.8291
No log	43.0	129	0.7181	0.5030	0.7183	0.8475
No log	44.0	132	0.7334	0.5419	0.7334	0.8564
No log	45.0	135	0.6963	0.5129	0.6964	0.8345
No log	46.0	138	0.7060	0.5400	0.7060	0.8402
No log	47.0	141	0.7268	0.5268	0.7269	0.8526
No log	48.0	144	0.7264	0.5520	0.7264	0.8523
No log	49.0	147	0.7289	0.5155	0.7290	0.8538
No log	50.0	150	0.7234	0.5304	0.7235	0.8506
No log	51.0	153	0.7201	0.5308	0.7203	0.8487
No log	52.0	156	0.7471	0.5476	0.7473	0.8644
No log	53.0	159	0.7771	0.5325	0.7773	0.8816
No log	54.0	162	0.7313	0.5453	0.7313	0.8552
No log	55.0	165	0.7713	0.5364	0.7715	0.8783
No log	56.0	168	0.7119	0.5324	0.7120	0.8438
No log	57.0	171	0.6981	0.5277	0.6982	0.8356
No log	58.0	174	0.6808	0.5315	0.6809	0.8251
No log	59.0	177	0.7281	0.5278	0.7283	0.8534
No log	60.0	180	0.7161	0.5308	0.7162	0.8463
No log	61.0	183	0.6906	0.5503	0.6906	0.8310
No log	62.0	186	0.6935	0.5478	0.6935	0.8328
No log	63.0	189	0.7146	0.5387	0.7146	0.8453
No log	64.0	192	0.7339	0.5494	0.7339	0.8567
No log	65.0	195	0.7206	0.5520	0.7205	0.8488
No log	66.0	198	0.7157	0.5313	0.7157	0.8460
No log	67.0	201	0.7137	0.5517	0.7138	0.8448
No log	68.0	204	0.7223	0.5413	0.7223	0.8499
No log	69.0	207	0.7270	0.5293	0.7271	0.8527
No log	70.0	210	0.7322	0.5255	0.7322	0.8557
No log	71.0	213	0.7575	0.5231	0.7576	0.8704
No log	72.0	216	0.7151	0.5245	0.7151	0.8456
No log	73.0	219	0.7225	0.5367	0.7225	0.8500
No log	74.0	222	0.7071	0.5235	0.7072	0.8409
No log	75.0	225	0.7284	0.5224	0.7285	0.8535
No log	76.0	228	0.7494	0.5213	0.7496	0.8658
No log	77.0	231	0.7600	0.5429	0.7601	0.8718
No log	78.0	234	0.7678	0.5313	0.7679	0.8763
No log	79.0	237	0.7658	0.5307	0.7658	0.8751
No log	80.0	240	0.7530	0.5317	0.7531	0.8678
No log	81.0	243	0.7520	0.5298	0.7520	0.8672
No log	82.0	246	0.7588	0.5277	0.7588	0.8711
No log	83.0	249	0.7345	0.5309	0.7346	0.8571
No log	84.0	252	0.7265	0.5353	0.7265	0.8524
No log	85.0	255	0.7257	0.5357	0.7258	0.8519

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2