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	11.7256	0.0257	11.7256	3.4243
No log	2.0	2	9.9255	0.0053	9.9255	3.1505
No log	3.0	3	8.5796	0.0054	8.5796	2.9291
No log	4.0	4	7.4738	0.0	7.4738	2.7338
No log	5.0	5	6.6817	0.0	6.6817	2.5849
No log	6.0	6	5.8882	0.0295	5.8882	2.4266
No log	7.0	7	5.2303	0.0262	5.2303	2.2870
No log	8.0	8	4.6708	0.0140	4.6708	2.1612
No log	9.0	9	3.9989	0.0115	3.9989	1.9997
No log	10.0	10	3.5540	0.0039	3.5540	1.8852
No log	11.0	11	2.9519	0.0039	2.9519	1.7181
No log	12.0	12	2.4926	0.0903	2.4926	1.5788
No log	13.0	13	2.1926	0.0771	2.1926	1.4807
No log	14.0	14	1.8869	0.0506	1.8869	1.3736
No log	15.0	15	1.6432	0.0484	1.6432	1.2819
No log	16.0	16	1.3941	0.0419	1.3941	1.1807
No log	17.0	17	1.2488	0.0316	1.2488	1.1175
No log	18.0	18	1.1133	0.0316	1.1133	1.0551
No log	19.0	19	1.0044	0.0316	1.0044	1.0022
No log	20.0	20	0.9147	0.0581	0.9147	0.9564
No log	21.0	21	0.8478	0.2242	0.8478	0.9207
No log	22.0	22	0.8359	0.1952	0.8359	0.9143
No log	23.0	23	0.8031	0.1369	0.8031	0.8962
No log	24.0	24	0.7368	0.1402	0.7368	0.8584
No log	25.0	25	0.7049	0.1917	0.7049	0.8396
No log	26.0	26	0.6923	0.1858	0.6923	0.8321
No log	27.0	27	0.7002	0.1402	0.7002	0.8368
No log	28.0	28	0.7483	0.1177	0.7483	0.8650
No log	29.0	29	0.7921	0.1131	0.7921	0.8900
No log	30.0	30	0.7471	0.1632	0.7471	0.8644
No log	31.0	31	0.6267	0.2615	0.6267	0.7916
No log	32.0	32	0.5722	0.3217	0.5722	0.7564
No log	33.0	33	0.5758	0.3288	0.5758	0.7588
No log	34.0	34	0.6690	0.4436	0.6690	0.8179
No log	35.0	35	0.7163	0.4781	0.7163	0.8464
No log	36.0	36	0.6334	0.5104	0.6334	0.7959
No log	37.0	37	0.4881	0.5096	0.4881	0.6986
No log	38.0	38	0.4662	0.5441	0.4662	0.6828
No log	39.0	39	0.4646	0.5880	0.4646	0.6816
No log	40.0	40	0.5039	0.6250	0.5039	0.7098
No log	41.0	41	0.5235	0.6253	0.5235	0.7236
No log	42.0	42	0.5032	0.6518	0.5032	0.7094
No log	43.0	43	0.5078	0.6343	0.5078	0.7126
No log	44.0	44	0.5303	0.6186	0.5303	0.7282
No log	45.0	45	0.5214	0.6486	0.5214	0.7221
No log	46.0	46	0.5518	0.6607	0.5518	0.7429
No log	47.0	47	0.5468	0.6737	0.5468	0.7395
No log	48.0	48	0.5396	0.6654	0.5396	0.7346
No log	49.0	49	0.5419	0.6712	0.5419	0.7361
No log	50.0	50	0.5437	0.6708	0.5437	0.7373
No log	51.0	51	0.5353	0.6695	0.5353	0.7317
No log	52.0	52	0.5337	0.6653	0.5337	0.7306
No log	53.0	53	0.5523	0.6569	0.5523	0.7432
No log	54.0	54	0.5348	0.6575	0.5348	0.7313
No log	55.0	55	0.5622	0.6334	0.5622	0.7498
No log	56.0	56	0.5495	0.6260	0.5495	0.7413
No log	57.0	57	0.5301	0.6545	0.5301	0.7281
No log	58.0	58	0.6341	0.6274	0.6341	0.7963
No log	59.0	59	0.6711	0.6088	0.6711	0.8192
No log	60.0	60	0.5960	0.6358	0.5960	0.7720
No log	61.0	61	0.5468	0.6343	0.5468	0.7395
No log	62.0	62	0.5738	0.6026	0.5738	0.7575
No log	63.0	63	0.5693	0.6277	0.5693	0.7545
No log	64.0	64	0.5799	0.6374	0.5799	0.7615
No log	65.0	65	0.6062	0.6347	0.6062	0.7786
No log	66.0	66	0.5904	0.6317	0.5904	0.7684
No log	67.0	67	0.5824	0.6252	0.5824	0.7632

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0