--- 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: train split: train args: train metrics: - name: Accuracy type: accuracy value: 0.83 --- # 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: 0.6180 - Accuracy: 0.83 ## 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: 4 - eval_batch_size: 4 - 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: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6805 | 1.0 | 225 | 1.4984 | 0.64 | | 0.8767 | 2.0 | 450 | 1.0174 | 0.67 | | 0.9691 | 3.0 | 675 | 0.8688 | 0.73 | | 0.6252 | 4.0 | 900 | 0.7695 | 0.77 | | 0.2577 | 5.0 | 1125 | 0.6340 | 0.81 | | 0.076 | 6.0 | 1350 | 0.6358 | 0.8 | | 0.1941 | 7.0 | 1575 | 0.6180 | 0.83 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3