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	2	10.6036	0.0046	10.6036	3.2563
No log	2.0	4	8.5701	0.0	8.5702	2.9275
No log	3.0	6	6.0175	0.0475	6.0179	2.4531
No log	4.0	8	4.1413	0.0222	4.1417	2.0351
No log	5.0	10	3.9674	0.0117	3.9679	1.9920
No log	6.0	12	2.6642	0.0355	2.6646	1.6324
No log	7.0	14	2.0510	0.1439	2.0513	1.4322
No log	8.0	16	1.9599	0.1104	1.9603	1.4001
No log	9.0	18	1.4199	0.0384	1.4203	1.1918
No log	10.0	20	1.0882	0.0	1.0885	1.0433
No log	11.0	22	0.9248	0.0462	0.9252	0.9619
No log	12.0	24	0.8091	0.2688	0.8095	0.8997
No log	13.0	26	0.8578	0.2130	0.8581	0.9263
No log	14.0	28	0.7238	0.2989	0.7240	0.8509
No log	15.0	30	0.7649	0.3175	0.7652	0.8747
No log	16.0	32	0.8621	0.2910	0.8624	0.9287
No log	17.0	34	0.6420	0.4378	0.6422	0.8014
No log	18.0	36	0.6216	0.4750	0.6218	0.7886
No log	19.0	38	0.5587	0.4935	0.5589	0.7476
No log	20.0	40	0.6323	0.4808	0.6326	0.7953
No log	21.0	42	0.5375	0.4895	0.5376	0.7332
No log	22.0	44	0.5493	0.5013	0.5495	0.7413
No log	23.0	46	0.5666	0.5019	0.5668	0.7528
No log	24.0	48	0.5794	0.5177	0.5794	0.7612
No log	25.0	50	0.7647	0.4763	0.7645	0.8744
No log	26.0	52	0.6100	0.5510	0.6098	0.7809
No log	27.0	54	0.8331	0.4809	0.8328	0.9126
No log	28.0	56	0.7099	0.5208	0.7097	0.8424
No log	29.0	58	0.6141	0.5339	0.6138	0.7835
No log	30.0	60	0.6672	0.5267	0.6669	0.8167
No log	31.0	62	0.6527	0.5113	0.6524	0.8077
No log	32.0	64	0.6260	0.5316	0.6257	0.7910
No log	33.0	66	0.8563	0.4927	0.8560	0.9252
No log	34.0	68	0.8579	0.4849	0.8576	0.9261
No log	35.0	70	0.6321	0.5546	0.6318	0.7948
No log	36.0	72	0.6617	0.5584	0.6613	0.8132
No log	37.0	74	0.7132	0.4987	0.7129	0.8443
No log	38.0	76	0.6744	0.5047	0.6742	0.8211
No log	39.0	78	0.6515	0.4738	0.6514	0.8071
No log	40.0	80	0.6597	0.4532	0.6595	0.8121
No log	41.0	82	0.6428	0.4892	0.6426	0.8016
No log	42.0	84	0.6894	0.5326	0.6892	0.8302
No log	43.0	86	0.6671	0.5493	0.6668	0.8166
No log	44.0	88	0.7879	0.5227	0.7877	0.8875
No log	45.0	90	0.8064	0.5080	0.8061	0.8978
No log	46.0	92	0.6519	0.5542	0.6515	0.8071
No log	47.0	94	0.6519	0.5629	0.6515	0.8072
No log	48.0	96	0.6763	0.5556	0.6759	0.8222
No log	49.0	98	0.6562	0.5801	0.6558	0.8098
No log	50.0	100	0.6718	0.5648	0.6715	0.8194
No log	51.0	102	0.6481	0.5699	0.6477	0.8048
No log	52.0	104	0.6347	0.5656	0.6343	0.7964
No log	53.0	106	0.6463	0.5486	0.6459	0.8037
No log	54.0	108	0.6417	0.5365	0.6414	0.8009
No log	55.0	110	0.6390	0.5322	0.6387	0.7992
No log	56.0	112	0.6535	0.5393	0.6531	0.8081
No log	57.0	114	0.6340	0.5365	0.6337	0.7960
No log	58.0	116	0.6731	0.5574	0.6728	0.8203
No log	59.0	118	0.6484	0.5739	0.6480	0.8050
No log	60.0	120	0.6586	0.5771	0.6582	0.8113
No log	61.0	122	0.6410	0.5771	0.6407	0.8004
No log	62.0	124	0.6255	0.5762	0.6252	0.7907
No log	63.0	126	0.6153	0.5502	0.6150	0.7842
No log	64.0	128	0.6297	0.5460	0.6295	0.7934
No log	65.0	130	0.5992	0.5834	0.5988	0.7738
No log	66.0	132	0.6041	0.5928	0.6038	0.7770
No log	67.0	134	0.6441	0.5858	0.6438	0.8024
No log	68.0	136	0.6246	0.6128	0.6243	0.7901
No log	69.0	138	0.6322	0.5832	0.6318	0.7949
No log	70.0	140	0.6354	0.5793	0.6350	0.7969
No log	71.0	142	0.6338	0.5694	0.6334	0.7958
No log	72.0	144	0.6658	0.5728	0.6654	0.8157
No log	73.0	146	0.6476	0.5705	0.6472	0.8045
No log	74.0	148	0.6402	0.5866	0.6398	0.7999
No log	75.0	150	0.6394	0.5628	0.6390	0.7994
No log	76.0	152	0.6751	0.5706	0.6747	0.8214
No log	77.0	154	0.6791	0.5793	0.6787	0.8238
No log	78.0	156	0.6523	0.5729	0.6519	0.8074
No log	79.0	158	0.6485	0.5808	0.6481	0.8051
No log	80.0	160	0.6653	0.5695	0.6650	0.8155
No log	81.0	162	0.6464	0.5729	0.6460	0.8038
No log	82.0	164	0.6288	0.5720	0.6284	0.7927
No log	83.0	166	0.6145	0.5741	0.6141	0.7836
No log	84.0	168	0.6221	0.5734	0.6217	0.7885
No log	85.0	170	0.6359	0.5711	0.6355	0.7972
No log	86.0	172	0.6382	0.5677	0.6379	0.7987
No log	87.0	174	0.6272	0.5649	0.6268	0.7917
No log	88.0	176	0.6208	0.5532	0.6204	0.7876

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

ASAP_nosemantic_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/
ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2