vowelizer_1203_v2

This model is a fine-tuned version of google/canine-s on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0018
• Precision: 0.9989
• Recall: 0.9988
• F1: 0.9989
• Accuracy: 0.9995

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: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1589	1.0	1933	0.1247	0.9263	0.8773	0.9011	0.9577
0.1227	2.0	3866	0.0937	0.9453	0.9110	0.9278	0.9676
0.1019	3.0	5799	0.0738	0.9589	0.9261	0.9422	0.9743
0.0868	4.0	7732	0.0595	0.9654	0.9530	0.9592	0.9792
0.0745	5.0	9665	0.0470	0.9741	0.9609	0.9675	0.9833
0.0638	6.0	11598	0.0364	0.9799	0.9728	0.9764	0.9873
0.0529	7.0	13531	0.0282	0.9853	0.9748	0.9800	0.9899
0.0473	8.0	15464	0.0218	0.9894	0.9838	0.9866	0.9923
0.0381	9.0	17397	0.0170	0.9909	0.9895	0.9902	0.9940
0.0325	10.0	19330	0.0128	0.9936	0.9921	0.9928	0.9956
0.0284	11.0	21263	0.0100	0.9950	0.9938	0.9944	0.9965
0.0256	12.0	23196	0.0079	0.9959	0.9949	0.9954	0.9972
0.0222	13.0	25129	0.0058	0.9969	0.9965	0.9967	0.9980
0.0196	14.0	27062	0.0048	0.9974	0.9973	0.9974	0.9984
0.016	15.0	28995	0.0036	0.9979	0.9974	0.9977	0.9988
0.0143	16.0	30928	0.0030	0.9983	0.9981	0.9982	0.9990
0.0134	17.0	32861	0.0025	0.9986	0.9984	0.9985	0.9992
0.0117	18.0	34794	0.0021	0.9987	0.9986	0.9987	0.9993
0.0102	19.0	36727	0.0019	0.9987	0.9987	0.9987	0.9994
0.0098	20.0	38660	0.0018	0.9989	0.9988	0.9989	0.9995

Framework versions

• Transformers 4.28.0
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.13.3