learning_rate: 2.5e-05
train_batch_size: 16
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall	Accuracy
0.3736	0.8	20	0.2035	0.0482	0.1176	0.0303	0.9431
0.2416	1.6	40	0.1315	0.3044	0.2724	0.3449	0.9559
0.0684	2.4	60	0.1027	0.4766	0.4303	0.5341	0.9668
0.0588	3.2	80	0.0881	0.6067	0.5490	0.6778	0.9733
0.1194	4.0	100	0.0851	0.6805	0.6521	0.7114	0.9766
0.014	4.8	120	0.0800	0.7078	0.6512	0.7751	0.9769
0.0572	5.6	140	0.0815	0.7228	0.6882	0.7611	0.9788
0.0059	6.4	160	0.0910	0.7016	0.6408	0.7751	0.9767
0.0079	7.2	180	0.0946	0.6864	0.6237	0.7632	0.9760
0.0152	8.0	200	0.0981	0.7107	0.6494	0.7849	0.9760
0.0169	8.8	220	0.0954	0.702	0.6530	0.7589	0.9771
0.0027	9.6	240	0.0983	0.7214	0.6984	0.7459	0.9775
0.0107	10.4	260	0.1050	0.7141	0.6544	0.7859	0.9773
0.0029	11.2	280	0.1072	0.7139	0.6555	0.7838	0.9773
0.0056	12.0	300	0.1075	0.7216	0.6670	0.7859	0.9777
0.0048	12.8	320	0.1109	0.7245	0.6628	0.7989	0.9775
0.0033	13.6	340	0.1133	0.7242	0.6691	0.7892	0.9773
0.0021	14.4	360	0.1098	0.7247	0.6916	0.7611	0.9784
0.0057	15.2	380	0.1131	0.7223	0.6652	0.7903	0.9779
0.0014	16.0	400	0.1113	0.7319	0.6889	0.7805	0.9789
0.0098	16.8	420	0.1140	0.7207	0.6670	0.7838	0.9778
0.0019	17.6	440	0.1162	0.7177	0.6612	0.7849	0.9778
0.001	18.4	460	0.1194	0.7259	0.6682	0.7946	0.9780
0.0009	19.2	480	0.1174	0.7307	0.6860	0.7816	0.9784
0.001	20.0	500	0.1219	0.7267	0.6673	0.7978	0.9779
0.0016	20.8	520	0.1183	0.7312	0.6819	0.7881	0.9784
0.001	21.6	540	0.1187	0.7306	0.6850	0.7827	0.9784
0.0012	22.4	560	0.1227	0.7323	0.6752	0.8	0.9784
0.0011	23.2	580	0.1218	0.7212	0.6624	0.7914	0.9777
0.0012	24.0	600	0.1217	0.7243	0.6700	0.7881	0.9780
0.0006	24.8	620	0.1217	0.7296	0.6807	0.7859	0.9784
0.0006	25.6	640	0.1233	0.7272	0.6688	0.7968	0.9782
0.001	26.4	660	0.1206	0.7305	0.6807	0.7881	0.9787
0.0005	27.2	680	0.1207	0.7329	0.6842	0.7892	0.9786
0.0012	28.0	700	0.1211	0.7318	0.6822	0.7892	0.9786
0.0017	28.8	720	0.1225	0.7274	0.6706	0.7946	0.9784
0.001	29.6	740	0.1225	0.7274	0.6706	0.7946	0.9784

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1

prinsINT/deberta-v3-base-uner-down-synth400

deberta-v3-base-uner-down-synth400

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

prinsINT/
deberta-v3-base-uner-down-synth400