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	11.5206	0.0051	11.5206	3.3942
No log	2.0	2	10.4870	0.0018	10.4870	3.2384
No log	3.0	3	10.2393	0.0	10.2393	3.1999
No log	4.0	4	9.9063	0.0	9.9063	3.1474
No log	5.0	5	9.4885	0.0	9.4885	3.0803
No log	6.0	6	8.9943	0.0	8.9943	2.9990
No log	7.0	7	8.4580	0.0	8.4580	2.9083
No log	8.0	8	7.9194	0.0	7.9194	2.8141
No log	9.0	9	7.3635	0.0	7.3635	2.7136
No log	10.0	10	6.8247	0.0	6.8247	2.6124
No log	11.0	11	6.3895	0.0	6.3895	2.5277
No log	12.0	12	6.0403	0.0092	6.0403	2.4577
No log	13.0	13	5.5936	0.0403	5.5936	2.3651
No log	14.0	14	5.0138	0.0114	5.0138	2.2391
No log	15.0	15	4.5558	0.0113	4.5558	2.1344
No log	16.0	16	4.1613	0.0089	4.1613	2.0399
No log	17.0	17	3.8520	0.0079	3.8520	1.9627
No log	18.0	18	3.5832	0.0079	3.5832	1.8929
No log	19.0	19	3.2357	0.0079	3.2357	1.7988
No log	20.0	20	2.8994	0.0079	2.8994	1.7028
No log	21.0	21	2.5429	0.0471	2.5429	1.5946
No log	22.0	22	2.2601	0.1524	2.2601	1.5034
No log	23.0	23	2.1380	0.1352	2.1380	1.4622
No log	24.0	24	1.8445	0.1031	1.8445	1.3581
No log	25.0	25	1.6839	0.1280	1.6839	1.2977
No log	26.0	26	1.5547	0.1145	1.5547	1.2469
No log	27.0	27	1.3737	0.0841	1.3737	1.1720
No log	28.0	28	1.3151	0.0381	1.3151	1.1468
No log	29.0	29	1.1984	0.0316	1.1984	1.0947
No log	30.0	30	1.0213	0.0381	1.0213	1.0106
No log	31.0	31	0.9536	0.0381	0.9536	0.9765
No log	32.0	32	0.8871	0.0701	0.8871	0.9418
No log	33.0	33	0.8281	0.2893	0.8281	0.9100
No log	34.0	34	0.7723	0.4051	0.7723	0.8788
No log	35.0	35	0.7272	0.4062	0.7272	0.8528
No log	36.0	36	0.7005	0.3641	0.7005	0.8370
No log	37.0	37	0.6932	0.3190	0.6932	0.8326
No log	38.0	38	0.6672	0.3481	0.6672	0.8168
No log	39.0	39	0.6411	0.3999	0.6411	0.8007
No log	40.0	40	0.6258	0.4434	0.6258	0.7911
No log	41.0	41	0.6208	0.4436	0.6208	0.7879
No log	42.0	42	0.6159	0.4519	0.6159	0.7848
No log	43.0	43	0.6159	0.4494	0.6159	0.7848
No log	44.0	44	0.6244	0.4723	0.6244	0.7902
No log	45.0	45	0.6513	0.4880	0.6513	0.8070
No log	46.0	46	0.6774	0.4862	0.6774	0.8230
No log	47.0	47	0.6685	0.5321	0.6685	0.8176
No log	48.0	48	0.6726	0.5644	0.6726	0.8201
No log	49.0	49	0.6845	0.5719	0.6845	0.8273
No log	50.0	50	0.7049	0.5684	0.7049	0.8396
No log	51.0	51	0.7361	0.5746	0.7361	0.8580
No log	52.0	52	0.7512	0.5858	0.7512	0.8667
No log	53.0	53	0.7418	0.5990	0.7418	0.8613
No log	54.0	54	0.7413	0.6012	0.7413	0.8610
No log	55.0	55	0.7383	0.6010	0.7383	0.8592
No log	56.0	56	0.7394	0.6017	0.7394	0.8599
No log	57.0	57	0.7383	0.6030	0.7383	0.8593
No log	58.0	58	0.6965	0.6031	0.6965	0.8346
No log	59.0	59	0.6801	0.6072	0.6801	0.8247
No log	60.0	60	0.7399	0.6220	0.7399	0.8602
No log	61.0	61	0.8313	0.5896	0.8313	0.9118
No log	62.0	62	0.7532	0.6178	0.7532	0.8679
No log	63.0	63	0.6516	0.6304	0.6516	0.8072
No log	64.0	64	0.6410	0.6294	0.6410	0.8006
No log	65.0	65	0.6764	0.6242	0.6764	0.8225
No log	66.0	66	0.7431	0.6154	0.7431	0.8620
No log	67.0	67	0.6950	0.6131	0.6950	0.8337
No log	68.0	68	0.5870	0.6471	0.5870	0.7661
No log	69.0	69	0.5717	0.6392	0.5717	0.7561
No log	70.0	70	0.6005	0.6206	0.6005	0.7749
No log	71.0	71	0.6885	0.6143	0.6885	0.8298
No log	72.0	72	0.6824	0.6184	0.6824	0.8261
No log	73.0	73	0.6214	0.6236	0.6214	0.7883
No log	74.0	74	0.6401	0.6219	0.6401	0.8000
No log	75.0	75	0.6433	0.6200	0.6433	0.8021
No log	76.0	76	0.6643	0.6245	0.6643	0.8150
No log	77.0	77	0.6398	0.6211	0.6398	0.7999
No log	78.0	78	0.6626	0.6194	0.6626	0.8140
No log	79.0	79	0.7201	0.6102	0.7201	0.8486
No log	80.0	80	0.7300	0.6103	0.7300	0.8544
No log	81.0	81	0.6754	0.6111	0.6754	0.8218
No log	82.0	82	0.6322	0.6351	0.6322	0.7951
No log	83.0	83	0.6296	0.6357	0.6296	0.7935
No log	84.0	84	0.6568	0.6181	0.6568	0.8104
No log	85.0	85	0.7317	0.6088	0.7317	0.8554
No log	86.0	86	0.8357	0.6025	0.8357	0.9142
No log	87.0	87	0.8722	0.5930	0.8722	0.9339
No log	88.0	88	0.8408	0.6025	0.8408	0.9169

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version5ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Version5ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version5ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4