learning_rate: 4.095814889546421e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7169	0.0347	10	0.7240	0.4729
0.7286	0.0694	20	0.7201	0.4729
0.7329	0.1042	30	0.7146	0.4693
0.682	0.1389	40	0.7111	0.4657
0.7033	0.1736	50	0.7098	0.4693
0.7306	0.2083	60	0.7084	0.4693
0.6959	0.2431	70	0.7069	0.4693
0.7061	0.2778	80	0.7071	0.4693
0.6775	0.3125	90	0.7062	0.4693
0.7102	0.3472	100	0.7044	0.4657
0.6862	0.3819	110	0.7045	0.4693
0.7233	0.4167	120	0.7021	0.4657
0.7428	0.4514	130	0.6989	0.4765
0.6802	0.4861	140	0.6966	0.4729
0.7106	0.5208	150	0.6968	0.4693
0.7177	0.5556	160	0.6965	0.4729
0.7069	0.5903	170	0.6957	0.4729
0.7056	0.625	180	0.6949	0.4657
0.6949	0.6597	190	0.6941	0.4801
0.6938	0.6944	200	0.6927	0.5162
0.6818	0.7292	210	0.6917	0.5487
0.699	0.7639	220	0.6905	0.5415
0.6907	0.7986	230	0.6907	0.5235
0.6641	0.8333	240	0.6911	0.5487
0.6955	0.8681	250	0.6910	0.5487
0.7175	0.9028	260	0.6905	0.5523
0.6974	0.9375	270	0.6892	0.5523
0.6739	0.9722	280	0.6878	0.5704
0.6852	1.0069	290	0.6874	0.5740
0.7105	1.0417	300	0.6867	0.5812
0.6908	1.0764	310	0.6860	0.5704
0.6688	1.1111	320	0.6858	0.5451
0.6757	1.1458	330	0.6862	0.5632
0.691	1.1806	340	0.6861	0.5704
0.6933	1.2153	350	0.6858	0.5776
0.6982	1.25	360	0.6857	0.5560
0.702	1.2847	370	0.6849	0.5668
0.6979	1.3194	380	0.6827	0.5957
0.6975	1.3542	390	0.6805	0.5632
0.6612	1.3889	400	0.6794	0.5884
0.7044	1.4236	410	0.6787	0.5812
0.6882	1.4583	420	0.6776	0.5740
0.6798	1.4931	430	0.6764	0.5812
0.6644	1.5278	440	0.6752	0.5668
0.6692	1.5625	450	0.6758	0.5560
0.676	1.5972	460	0.6763	0.5523
0.6879	1.6319	470	0.6745	0.5560
0.678	1.6667	480	0.6684	0.5740
0.7134	1.7014	490	0.6665	0.6065
0.6746	1.7361	500	0.6663	0.6137
0.6982	1.7708	510	0.6663	0.6029
0.6539	1.8056	520	0.6657	0.6065
0.6674	1.8403	530	0.6661	0.5957
0.625	1.875	540	0.6692	0.5812
0.6769	1.9097	550	0.6676	0.5812
0.6633	1.9444	560	0.6677	0.5812
0.7023	1.9792	570	0.6636	0.5957
0.6377	2.0139	580	0.6618	0.6101
0.6475	2.0486	590	0.6630	0.5993
0.6318	2.0833	600	0.6629	0.6029
0.6383	2.1181	610	0.6644	0.5957
0.6333	2.1528	620	0.6682	0.5812
0.6567	2.1875	630	0.6728	0.5776
0.6708	2.2222	640	0.6731	0.5812
0.6546	2.2569	650	0.6667	0.5884
0.6471	2.2917	660	0.6636	0.5957
0.6242	2.3264	670	0.6609	0.6029
0.6628	2.3611	680	0.6585	0.6101
0.6774	2.3958	690	0.6566	0.6173
0.6412	2.4306	700	0.6573	0.6209
0.6761	2.4653	710	0.6564	0.6101
0.6391	2.5	720	0.6559	0.6101
0.5988	2.5347	730	0.6546	0.6137
0.6464	2.5694	740	0.6553	0.6209
0.621	2.6042	750	0.6553	0.6209
0.6674	2.6389	760	0.6547	0.6282
0.6186	2.6736	770	0.6551	0.6209
0.6639	2.7083	780	0.6561	0.6137
0.6554	2.7431	790	0.6555	0.6137
0.6279	2.7778	800	0.6560	0.6101
0.666	2.8125	810	0.6566	0.6101
0.6205	2.8472	820	0.6572	0.6101
0.6281	2.8819	830	0.6574	0.6137
0.6376	2.9167	840	0.6572	0.6065
0.6138	2.9514	850	0.6571	0.6065
0.6416	2.9861	860	0.6572	0.6065

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

innsolit/Part1_TrainerHPO

Part1_TrainerHPO

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

innsolit/
Part1_TrainerHPO