distilhubert-finetuned-speech_commands-finetuned-gtzan
This model is a fine-tuned version of imrajeshkr/distilhubert-finetuned-speech_commands on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0934
- Precision: 0.9759
- Recall: 0.9749
- F1: 0.9749
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
---|---|---|---|---|---|---|
0.2713 | 1.0 | 1216 | 0.2523 | 0.9172 | 0.9267 | 0.9166 |
0.137 | 2.0 | 2432 | 0.1119 | 0.9685 | 0.9667 | 0.9664 |
0.0295 | 3.0 | 3648 | 0.0977 | 0.9726 | 0.9703 | 0.9701 |
0.0037 | 4.0 | 4864 | 0.0956 | 0.9743 | 0.9733 | 0.9732 |
0.052 | 5.0 | 6080 | 0.0934 | 0.9759 | 0.9749 | 0.9749 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
ntu-spml/distilhubertEvaluation results
- Precision on audiofoldertest set self-reported0.976
- Recall on audiofoldertest set self-reported0.975
- F1 on audiofoldertest set self-reported0.975