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	12.4330	0.0	12.4329	3.5260
No log	2.0	2	11.1051	0.0102	11.1052	3.3324
No log	3.0	3	9.9620	0.0	9.9622	3.1563
No log	4.0	4	8.5543	0.0	8.5547	2.9248
No log	5.0	5	7.8087	0.0	7.8091	2.7945
No log	6.0	6	7.1320	0.0	7.1323	2.6706
No log	7.0	7	6.3236	-0.0005	6.3241	2.5148
No log	8.0	8	5.4935	0.0423	5.4937	2.3439
No log	9.0	9	4.9270	0.0141	4.9272	2.2197
No log	10.0	10	4.6864	0.0	4.6870	2.1650
No log	11.0	11	4.2629	0.0	4.2635	2.0648
No log	12.0	12	3.6691	0.0	3.6696	1.9156
No log	13.0	13	3.3983	0.0	3.3986	1.8435
No log	14.0	14	3.0363	0.0	3.0367	1.7426
No log	15.0	15	2.7840	0.0	2.7845	1.6687
No log	16.0	16	2.5453	0.0027	2.5458	1.5955
No log	17.0	17	2.1832	0.1195	2.1837	1.4777
No log	18.0	18	1.9468	0.1429	1.9472	1.3954
No log	19.0	19	1.7177	0.0910	1.7181	1.3108
No log	20.0	20	1.5964	0.0213	1.5968	1.2637
No log	21.0	21	1.4336	0.0213	1.4341	1.1975
No log	22.0	22	1.2220	0.0107	1.2224	1.1056
No log	23.0	23	1.1023	0.0107	1.1027	1.0501
No log	24.0	24	1.0213	0.0107	1.0217	1.0108
No log	25.0	25	0.9346	0.0	0.9350	0.9669
No log	26.0	26	0.8746	0.0086	0.8750	0.9354
No log	27.0	27	0.7842	0.3803	0.7845	0.8857
No log	28.0	28	0.7520	0.3349	0.7523	0.8674
No log	29.0	29	0.7621	0.2537	0.7625	0.8732
No log	30.0	30	0.7167	0.2751	0.7170	0.8468
No log	31.0	31	0.6657	0.3259	0.6659	0.8160
No log	32.0	32	0.6782	0.2594	0.6784	0.8236
No log	33.0	33	0.7287	0.2260	0.7288	0.8537
No log	34.0	34	0.6784	0.2300	0.6785	0.8237
No log	35.0	35	0.6240	0.3072	0.6242	0.7901
No log	36.0	36	0.6270	0.2555	0.6272	0.7919
No log	37.0	37	0.8132	0.1984	0.8131	0.9017
No log	38.0	38	0.8579	0.1944	0.8578	0.9262
No log	39.0	39	0.7405	0.2270	0.7403	0.8604
No log	40.0	40	0.5869	0.3228	0.5870	0.7662
No log	41.0	41	0.5736	0.3373	0.5737	0.7574
No log	42.0	42	0.6464	0.4428	0.6462	0.8039
No log	43.0	43	0.8871	0.3794	0.8867	0.9416
No log	44.0	44	0.9204	0.3758	0.9200	0.9591
No log	45.0	45	0.7504	0.4750	0.7499	0.8659
No log	46.0	46	0.5367	0.5247	0.5365	0.7325
No log	47.0	47	0.5097	0.5419	0.5095	0.7138
No log	48.0	48	0.5989	0.5598	0.5984	0.7735
No log	49.0	49	0.7934	0.5003	0.7925	0.8902
No log	50.0	50	0.8026	0.5128	0.8017	0.8954
No log	51.0	51	0.6282	0.5847	0.6274	0.7921
No log	52.0	52	0.4550	0.6087	0.4547	0.6743
No log	53.0	53	0.4402	0.6249	0.4399	0.6633
No log	54.0	54	0.4864	0.6163	0.4859	0.6971
No log	55.0	55	0.7057	0.5848	0.7046	0.8394
No log	56.0	56	0.8058	0.5637	0.8045	0.8970
No log	57.0	57	0.7308	0.6025	0.7297	0.8542
No log	58.0	58	0.5594	0.6341	0.5587	0.7475
No log	59.0	59	0.4759	0.6523	0.4754	0.6895
No log	60.0	60	0.4889	0.6464	0.4885	0.6989
No log	61.0	61	0.6071	0.6284	0.6064	0.7787
No log	62.0	62	0.7460	0.5927	0.7451	0.8632
No log	63.0	63	0.7505	0.5914	0.7496	0.8658
No log	64.0	64	0.6228	0.6309	0.6222	0.7888
No log	65.0	65	0.6276	0.6334	0.6269	0.7917
No log	66.0	66	0.7236	0.6051	0.7227	0.8501
No log	67.0	67	0.7289	0.6120	0.7280	0.8532
No log	68.0	68	0.6227	0.6392	0.6219	0.7886
No log	69.0	69	0.5776	0.6391	0.5769	0.7595
No log	70.0	70	0.5056	0.6601	0.5050	0.7107
No log	71.0	71	0.5153	0.6641	0.5147	0.7174
No log	72.0	72	0.5880	0.6330	0.5872	0.7663
No log	73.0	73	0.6977	0.6027	0.6967	0.8347
No log	74.0	74	0.7025	0.6031	0.7015	0.8376
No log	75.0	75	0.6189	0.6395	0.6181	0.7862
No log	76.0	76	0.4991	0.6691	0.4986	0.7061
No log	77.0	77	0.4731	0.6761	0.4726	0.6875
No log	78.0	78	0.4907	0.6665	0.4901	0.7001
No log	79.0	79	0.5618	0.6569	0.5611	0.7490
No log	80.0	80	0.6009	0.6478	0.6002	0.7747
No log	81.0	81	0.5982	0.6479	0.5974	0.7729
No log	82.0	82	0.5476	0.6587	0.5470	0.7396
No log	83.0	83	0.5308	0.6612	0.5302	0.7282
No log	84.0	84	0.4901	0.6634	0.4896	0.6997
No log	85.0	85	0.4893	0.6655	0.4887	0.6991
No log	86.0	86	0.5197	0.6564	0.5191	0.7205
No log	87.0	87	0.5890	0.6431	0.5883	0.7670
No log	88.0	88	0.6219	0.6429	0.6212	0.7881
No log	89.0	89	0.6346	0.6417	0.6338	0.7961
No log	90.0	90	0.6128	0.6444	0.6121	0.7824
No log	91.0	91	0.5802	0.6488	0.5796	0.7613
No log	92.0	92	0.5797	0.6471	0.5791	0.7610
No log	93.0	93	0.5751	0.6497	0.5744	0.7579
No log	94.0	94	0.5531	0.6622	0.5525	0.7433
No log	95.0	95	0.5519	0.6622	0.5513	0.7425
No log	96.0	96	0.5634	0.6531	0.5627	0.7502
No log	97.0	97	0.5826	0.6480	0.5820	0.7629

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version_weird_ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

Version_weird_ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version_weird_ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2