| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: ntu-spml/distilhubert |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - marsyas/gtzan |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: distilhubert-finetuned-gtzan |
| | results: |
| | - task: |
| | name: Audio Classification |
| | type: audio-classification |
| | dataset: |
| | name: GTZAN |
| | type: marsyas/gtzan |
| | config: default |
| | split: None |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.87 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # distilhubert-finetuned-gtzan |
| |
|
| | This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5071 |
| | - Accuracy: 0.87 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 15 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 1.7065 | 1.0 | 113 | 1.5003 | 0.61 | |
| | | 1.0785 | 2.0 | 226 | 1.0084 | 0.69 | |
| | | 0.8457 | 3.0 | 339 | 0.7742 | 0.79 | |
| | | 0.6696 | 4.0 | 452 | 0.6197 | 0.82 | |
| | | 0.5859 | 5.0 | 565 | 0.5071 | 0.87 | |
| | | 0.3813 | 6.0 | 678 | 0.5068 | 0.85 | |
| | | 0.4032 | 7.0 | 791 | 0.4872 | 0.87 | |
| | | 0.2352 | 8.0 | 904 | 0.5913 | 0.83 | |
| | | 0.1345 | 9.0 | 1017 | 0.6382 | 0.84 | |
| | | 0.1871 | 10.0 | 1130 | 0.5928 | 0.87 | |
| | | 0.1533 | 11.0 | 1243 | 0.5992 | 0.86 | |
| | | 0.108 | 12.0 | 1356 | 0.6503 | 0.83 | |
| | | 0.0642 | 13.0 | 1469 | 0.6233 | 0.86 | |
| | | 0.0419 | 14.0 | 1582 | 0.6289 | 0.86 | |
| | | 0.0461 | 15.0 | 1695 | 0.6338 | 0.87 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.54.1 |
| | - Pytorch 2.9.0.dev20250731+cu128 |
| | - Datasets 3.6.0 |
| | - Tokenizers 0.21.4 |
| | |
