--- 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: train args: default metrics: - name: Accuracy type: accuracy value: 0.86 --- <!-- 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: 0.5280 - 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 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0395 | 1.0 | 113 | 1.8776 | 0.45 | | 1.3755 | 2.0 | 226 | 1.3103 | 0.61 | | 1.0674 | 3.0 | 339 | 0.9634 | 0.79 | | 0.8273 | 4.0 | 452 | 0.8435 | 0.77 | | 0.6761 | 5.0 | 565 | 0.6378 | 0.81 | | 0.4143 | 6.0 | 678 | 0.5439 | 0.84 | | 0.4146 | 7.0 | 791 | 0.5217 | 0.84 | | 0.1978 | 8.0 | 904 | 0.5172 | 0.85 | | 0.2495 | 9.0 | 1017 | 0.5295 | 0.84 | | 0.1658 | 10.0 | 1130 | 0.5280 | 0.86 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3