salt_language_Classification

This model is a fine-tuned version of google/t5-efficient-tiny on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.0615
Accuracy: 0.9782
Precision: 0.9787
Recall: 0.9782
F1: 0.9782

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 20000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.2011	0.025	500	0.4979	0.8733	0.9001	0.8733	0.8714
0.234	0.05	1000	0.1886	0.9345	0.9354	0.9345	0.9345
0.2083	0.075	1500	0.1833	0.9328	0.9391	0.9328	0.9328
0.1838	0.1	2000	0.1457	0.9476	0.9479	0.9476	0.9475
0.1737	0.125	2500	0.1659	0.9409	0.9438	0.9409	0.9411
0.1591	0.15	3000	0.1450	0.9516	0.9524	0.9516	0.9517
0.1571	0.175	3500	0.1351	0.9459	0.9485	0.9459	0.9461
0.1513	0.2	4000	0.1510	0.9456	0.9515	0.9456	0.9460
0.1439	0.225	4500	0.1339	0.9546	0.9578	0.9546	0.9547
0.1394	0.25	5000	0.1052	0.9657	0.9658	0.9657	0.9656
0.1472	0.275	5500	0.1088	0.9610	0.9629	0.9610	0.9609
0.1385	0.3	6000	0.0792	0.9694	0.9696	0.9694	0.9694
0.1349	0.325	6500	0.1063	0.9610	0.9632	0.9610	0.9613
0.1215	0.35	7000	0.0855	0.9688	0.9694	0.9688	0.9687
0.133	0.375	7500	0.1049	0.9630	0.9640	0.9630	0.9630
0.1226	0.4	8000	0.0938	0.9667	0.9675	0.9667	0.9667
0.1222	0.425	8500	0.1134	0.9570	0.9604	0.9570	0.9573
0.1165	0.45	9000	0.0997	0.9688	0.9697	0.9688	0.9687
0.1174	0.475	9500	0.1002	0.9661	0.9680	0.9661	0.9659
0.1165	0.5	10000	0.0807	0.9728	0.9728	0.9728	0.9728
0.1065	0.525	10500	0.0750	0.9745	0.9754	0.9745	0.9746
0.1089	0.55	11000	0.0896	0.9688	0.9703	0.9688	0.9689
0.1125	0.575	11500	0.0632	0.9782	0.9787	0.9782	0.9782
0.11	0.6	12000	0.0775	0.9691	0.9708	0.9691	0.9692
0.1028	0.625	12500	0.0833	0.9698	0.9708	0.9698	0.9698
0.1052	0.65	13000	0.0663	0.9751	0.9755	0.9751	0.9751
0.1068	0.675	13500	0.0648	0.9772	0.9774	0.9772	0.9772
0.1029	0.7	14000	0.0962	0.9688	0.9706	0.9688	0.9689
0.1014	0.725	14500	0.0686	0.9772	0.9775	0.9772	0.9771
0.0978	0.75	15000	0.0802	0.9745	0.9752	0.9745	0.9745
0.095	0.775	15500	0.0646	0.9758	0.9763	0.9758	0.9758
0.0996	0.8	16000	0.0711	0.9758	0.9761	0.9758	0.9758
0.0967	0.825	16500	0.0683	0.9761	0.9768	0.9761	0.9761
0.0939	0.85	17000	0.0572	0.9792	0.9795	0.9792	0.9791
0.0966	0.875	17500	0.0527	0.9792	0.9794	0.9792	0.9791
0.0925	0.9	18000	0.0581	0.9798	0.9802	0.9798	0.9799
0.0945	0.925	18500	0.0693	0.9768	0.9776	0.9768	0.9768
0.0923	0.95	19000	0.0615	0.9785	0.9790	0.9785	0.9785
0.0896	0.975	19500	0.0643	0.9758	0.9766	0.9758	0.9758
0.0979	1.0	20000	0.0619	0.9765	0.9770	0.9765	0.9765

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

yigagilbert/salt_language_Classification

salt_language_Classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model