canine_vowelizer_2105_v6

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: 0.1704
• Precision: 0.9998
• Recall: 0.9998
• F1: 0.9998
• Accuracy: 0.9391

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4826	1.0	3885	0.4310	0.9997	0.9998	0.9997	0.8467
0.4118	2.0	7770	0.3556	0.9997	0.9998	0.9997	0.8748
0.369	3.0	11655	0.3126	0.9997	0.9998	0.9997	0.8893
0.339	4.0	15540	0.2811	0.9997	0.9998	0.9998	0.9014
0.3192	5.0	19425	0.2589	0.9997	0.9998	0.9998	0.9095
0.3052	6.0	23310	0.2399	0.9997	0.9998	0.9998	0.9157
0.281	7.0	27195	0.2252	0.9997	0.9998	0.9998	0.9207
0.2749	8.0	31080	0.2117	0.9998	0.9998	0.9998	0.9248
0.2589	9.0	34965	0.2011	0.9998	0.9998	0.9998	0.9285
0.253	10.0	38850	0.1940	0.9998	0.9998	0.9998	0.9314
0.2428	11.0	42735	0.1842	0.9998	0.9998	0.9998	0.9348
0.2433	12.0	46620	0.1783	0.9998	0.9998	0.9998	0.9365
0.2265	13.0	50505	0.1751	0.9998	0.9998	0.9998	0.9375
0.2244	14.0	54390	0.1721	0.9998	0.9998	0.9998	0.9387
0.2203	15.0	58275	0.1704	0.9998	0.9998	0.9998	0.9391

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3