bsc_ai_thesis_torgo_model-1 – AI Model by Juardo | AlphaNeural AI

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bsc_ai_thesis_torgo_model-1

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audio-classification

generated_from_trainer

endpoints_compatible

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bsc_ai_thesis_torgo_model-1

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3532
Accuracy: 0.8625
Precision: 0.8349
Recall: 0.9055
F1: 0.8687

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6855	0.96	12	0.6603	0.6225	0.5772	0.9303	0.7124
0.5875	2.0	25	0.5249	0.785	0.7533	0.8507	0.7991
0.4858	2.96	37	0.5584	0.7575	0.6940	0.9254	0.7932
0.3951	4.0	50	0.5366	0.785	0.7220	0.9303	0.8130
0.3962	4.96	62	0.4707	0.805	0.7450	0.9303	0.8274
0.3069	6.0	75	0.4032	0.8325	0.8190	0.8557	0.8370
0.2973	6.96	87	0.3753	0.855	0.8593	0.8507	0.855
0.2585	8.0	100	0.3719	0.8625	0.8259	0.9204	0.8706
0.2365	8.96	112	0.3503	0.855	0.8357	0.8856	0.8599
0.2244	9.6	120	0.3532	0.8625	0.8349	0.9055	0.8687

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3