--- 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: 0.86 --- # 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: 1.0283 - Accuracy: 0.86 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0235 | 0.99 | 28 | 1.0778 | 0.83 | | 0.0072 | 1.98 | 56 | 1.0815 | 0.83 | | 0.0004 | 2.97 | 84 | 1.1249 | 0.82 | | 0.0003 | 4.0 | 113 | 1.1113 | 0.81 | | 0.0002 | 4.99 | 141 | 1.1442 | 0.79 | | 0.0137 | 5.98 | 169 | 1.0623 | 0.84 | | 0.0048 | 6.97 | 197 | 1.0193 | 0.86 | | 0.0087 | 8.0 | 226 | 1.0578 | 0.84 | | 0.0055 | 8.99 | 254 | 1.0279 | 0.86 | | 0.005 | 9.91 | 280 | 1.0283 | 0.86 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3