| --- |
| 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: all |
| split: train |
| args: all |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.88 |
| --- |
| |
| <!-- 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: 0.8080 |
| - Accuracy: 0.88 |
|
|
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 1.7263 | 1.0 | 113 | 1.6331 | 0.59 | |
| | 1.1167 | 2.0 | 226 | 1.2046 | 0.65 | |
| | 0.774 | 3.0 | 339 | 0.8365 | 0.8 | |
| | 0.6507 | 4.0 | 452 | 0.7015 | 0.81 | |
| | 0.5046 | 5.0 | 565 | 0.6722 | 0.81 | |
| | 0.2632 | 6.0 | 678 | 0.6743 | 0.82 | |
| | 0.203 | 7.0 | 791 | 0.7351 | 0.84 | |
| | 0.0902 | 8.0 | 904 | 0.5898 | 0.86 | |
| | 0.0215 | 9.0 | 1017 | 0.6213 | 0.87 | |
| | 0.0097 | 10.0 | 1130 | 0.6948 | 0.86 | |
| | 0.1171 | 11.0 | 1243 | 0.6228 | 0.87 | |
| | 0.0054 | 12.0 | 1356 | 0.7101 | 0.86 | |
| | 0.0035 | 13.0 | 1469 | 0.7626 | 0.87 | |
| | 0.0028 | 14.0 | 1582 | 0.7659 | 0.86 | |
| | 0.0027 | 15.0 | 1695 | 0.6993 | 0.87 | |
| | 0.0023 | 16.0 | 1808 | 0.7345 | 0.87 | |
| | 0.0023 | 17.0 | 1921 | 0.8363 | 0.86 | |
| | 0.0018 | 18.0 | 2034 | 0.7779 | 0.88 | |
| | 0.0018 | 19.0 | 2147 | 0.8275 | 0.87 | |
| | 0.0018 | 20.0 | 2260 | 0.8080 | 0.88 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.36.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.16.0 |
| - Tokenizers 0.15.0 |
| |
