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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	2	8.9046	0.0	8.9046	2.9841
No log	2.0	4	7.5568	0.0	7.5568	2.7490
No log	3.0	6	6.8754	0.0	6.8754	2.6221
No log	4.0	8	5.9363	0.0184	5.9363	2.4365
No log	5.0	10	5.0945	0.0115	5.0945	2.2571
No log	6.0	12	4.3012	0.0039	4.3012	2.0739
No log	7.0	14	3.5263	0.0	3.5263	1.8778
No log	8.0	16	2.7742	0.0	2.7742	1.6656
No log	9.0	18	2.1760	0.1058	2.1760	1.4751
No log	10.0	20	1.7650	0.0575	1.7650	1.3285
No log	11.0	22	1.3480	0.0316	1.3480	1.1610
No log	12.0	24	1.1603	0.0316	1.1603	1.0772
No log	13.0	26	1.6768	0.1195	1.6768	1.2949
No log	14.0	28	1.0456	0.0548	1.0456	1.0226
No log	15.0	30	0.9407	0.1163	0.9407	0.9699
No log	16.0	32	1.2061	0.1504	1.2061	1.0982
No log	17.0	34	0.7482	0.4012	0.7482	0.8650
No log	18.0	36	0.7348	0.4228	0.7348	0.8572
No log	19.0	38	0.9797	0.3286	0.9797	0.9898
No log	20.0	40	0.7183	0.4347	0.7183	0.8475
No log	21.0	42	0.6551	0.3615	0.6551	0.8094
No log	22.0	44	0.6768	0.4040	0.6768	0.8227
No log	23.0	46	0.9637	0.3459	0.9637	0.9817
No log	24.0	48	0.7064	0.4154	0.7064	0.8405
No log	25.0	50	0.6389	0.3661	0.6389	0.7993
No log	26.0	52	0.6657	0.4177	0.6657	0.8159
No log	27.0	54	1.1636	0.3086	1.1636	1.0787
No log	28.0	56	0.9001	0.3503	0.9001	0.9488
No log	29.0	58	0.6451	0.4195	0.6451	0.8032
No log	30.0	60	0.6849	0.4341	0.6849	0.8276
No log	31.0	62	0.9686	0.3745	0.9686	0.9842
No log	32.0	64	0.7435	0.4295	0.7435	0.8623
No log	33.0	66	0.7270	0.4432	0.7270	0.8526
No log	34.0	68	0.6510	0.5055	0.6510	0.8069
No log	35.0	70	0.7117	0.4495	0.7117	0.8436
No log	36.0	72	0.6471	0.4815	0.6471	0.8044
No log	37.0	74	0.6547	0.4727	0.6547	0.8091
No log	38.0	76	0.6719	0.4962	0.6719	0.8197
No log	39.0	78	0.7586	0.4578	0.7586	0.8710
No log	40.0	80	0.7598	0.4468	0.7598	0.8717
No log	41.0	82	0.7247	0.4552	0.7247	0.8513
No log	42.0	84	0.7476	0.4443	0.7476	0.8646
No log	43.0	86	0.6745	0.4864	0.6745	0.8213
No log	44.0	88	0.6658	0.4950	0.6658	0.8160
No log	45.0	90	0.6876	0.4997	0.6876	0.8292
No log	46.0	92	0.6994	0.4740	0.6994	0.8363
No log	47.0	94	0.6770	0.4850	0.6770	0.8228
No log	48.0	96	0.6755	0.5016	0.6755	0.8219
No log	49.0	98	0.8545	0.4773	0.8545	0.9244
No log	50.0	100	0.9804	0.4440	0.9804	0.9902
No log	51.0	102	0.7427	0.4906	0.7427	0.8618
No log	52.0	104	0.7361	0.4990	0.7361	0.8580
No log	53.0	106	0.7234	0.5020	0.7234	0.8505
No log	54.0	108	0.7987	0.4837	0.7987	0.8937
No log	55.0	110	0.7343	0.4847	0.7343	0.8569
No log	56.0	112	0.7466	0.4703	0.7466	0.8640
No log	57.0	114	0.8455	0.4762	0.8455	0.9195
No log	58.0	116	0.7661	0.4526	0.7661	0.8753
No log	59.0	118	0.7824	0.4755	0.7824	0.8845
No log	60.0	120	0.7412	0.4780	0.7412	0.8609
No log	61.0	122	0.7990	0.4855	0.7990	0.8938
No log	62.0	124	0.8535	0.4741	0.8535	0.9238
No log	63.0	126	0.7882	0.4915	0.7882	0.8878
No log	64.0	128	0.7076	0.5073	0.7076	0.8412
No log	65.0	130	0.6508	0.5083	0.6508	0.8067
No log	66.0	132	0.6318	0.5080	0.6318	0.7949
No log	67.0	134	0.6849	0.4986	0.6849	0.8276
No log	68.0	136	0.7586	0.5054	0.7586	0.8710
No log	69.0	138	0.7958	0.4987	0.7958	0.8921
No log	70.0	140	0.7019	0.5076	0.7019	0.8378
No log	71.0	142	0.6645	0.5146	0.6645	0.8152
No log	72.0	144	0.6674	0.5169	0.6674	0.8169
No log	73.0	146	0.6559	0.5179	0.6559	0.8099
No log	74.0	148	0.6816	0.5065	0.6816	0.8256
No log	75.0	150	0.6876	0.5072	0.6876	0.8292
No log	76.0	152	0.6619	0.4972	0.6619	0.8136
No log	77.0	154	0.7914	0.4935	0.7914	0.8896
No log	78.0	156	0.7702	0.4882	0.7702	0.8776
No log	79.0	158	0.6287	0.5156	0.6287	0.7929
No log	80.0	160	0.6209	0.5084	0.6209	0.7880
No log	81.0	162	0.6521	0.5118	0.6521	0.8075
No log	82.0	164	0.8774	0.4823	0.8774	0.9367
No log	83.0	166	0.7858	0.5035	0.7858	0.8864
No log	84.0	168	0.6128	0.4914	0.6128	0.7828
No log	85.0	170	0.6112	0.5083	0.6112	0.7818
No log	86.0	172	0.6536	0.4996	0.6536	0.8085
No log	87.0	174	0.8299	0.4950	0.8299	0.9110
No log	88.0	176	0.7865	0.4992	0.7865	0.8868
No log	89.0	178	0.6283	0.5177	0.6283	0.7927
No log	90.0	180	0.6214	0.5120	0.6214	0.7883
No log	91.0	182	0.6277	0.5266	0.6277	0.7923
No log	92.0	184	0.6244	0.5194	0.6244	0.7902
No log	93.0	186	0.6137	0.5239	0.6137	0.7834
No log	94.0	188	0.6581	0.5065	0.6581	0.8112
No log	95.0	190	0.7075	0.5104	0.7075	0.8411
No log	96.0	192	0.7054	0.5099	0.7054	0.8399
No log	97.0	194	0.6328	0.5086	0.6328	0.7955
No log	98.0	196	0.6259	0.5127	0.6259	0.7912
No log	99.0	198	0.6532	0.5140	0.6532	0.8082
No log	100.0	200	0.6218	0.5343	0.6218	0.7885
No log	101.0	202	0.6311	0.5165	0.6311	0.7944
No log	102.0	204	0.6570	0.5108	0.6570	0.8105
No log	103.0	206	0.6585	0.5171	0.6585	0.8115
No log	104.0	208	0.6762	0.5180	0.6762	0.8223
No log	105.0	210	0.7172	0.5043	0.7172	0.8469
No log	106.0	212	0.7105	0.4968	0.7105	0.8429
No log	107.0	214	0.6792	0.5198	0.6792	0.8241
No log	108.0	216	0.6868	0.5138	0.6868	0.8287
No log	109.0	218	0.7422	0.5041	0.7422	0.8615
No log	110.0	220	0.6995	0.5083	0.6995	0.8364
No log	111.0	222	0.6929	0.5170	0.6929	0.8324
No log	112.0	224	0.6830	0.5128	0.6830	0.8264
No log	113.0	226	0.6587	0.5181	0.6587	0.8116
No log	114.0	228	0.6777	0.5116	0.6777	0.8232
No log	115.0	230	0.6643	0.5199	0.6643	0.8150
No log	116.0	232	0.7057	0.4891	0.7057	0.8401
No log	117.0	234	0.7548	0.4915	0.7548	0.8688
No log	118.0	236	0.7038	0.4944	0.7038	0.8389
No log	119.0	238	0.6449	0.5132	0.6449	0.8031
No log	120.0	240	0.6413	0.5122	0.6413	0.8008
No log	121.0	242	0.6311	0.5262	0.6311	0.7944
No log	122.0	244	0.6421	0.5131	0.6421	0.8013
No log	123.0	246	0.6473	0.5083	0.6473	0.8045
No log	124.0	248	0.6309	0.5183	0.6309	0.7943
No log	125.0	250	0.6380	0.5051	0.6380	0.7987
No log	126.0	252	0.6787	0.5133	0.6787	0.8238
No log	127.0	254	0.6930	0.5030	0.6930	0.8324
No log	128.0	256	0.6774	0.5072	0.6774	0.8231
No log	129.0	258	0.6249	0.5094	0.6249	0.7905
No log	130.0	260	0.5897	0.5234	0.5897	0.7679

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version24NewTestASAP_FineTuningBERT_AugV24_k3_task1_organization_k3_k3_fold0

Version24NewTestASAP_FineTuningBERT_AugV24_k3_task1_organization_k3_k3_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version24NewTestASAP_FineTuningBERT_AugV24_k3_task1_organization_k3_k3_fold0