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yigagilbert/salt_language_Classification

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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saltlanguageClassification

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
—trainbatchsize: 64
—evalbatchsize: 64
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_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