Distilhubert Finetuned Gtzan

--- 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: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.81 --- <!-- 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: 0.6070 - Accuracy: 0.81 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9466 | 1.0 | 113 | 1.8833 | 0.49 | | 1.1906 | 2.0 | 226 | 1.2291 | 0.66 | | 1.096 | 3.0 | 339 | 1.0154 | 0.7 | | 0.697 | 4.0 | 452 | 0.8193 | 0.72 | | 0.6168 | 5.0 | 565 | 0.7191 | 0.79 | | 0.3712 | 6.0 | 678 | 0.6208 | 0.81 | | 0.2764 | 7.0 | 791 | 0.6111 | 0.79 | | 0.121 | 8.0 | 904 | 0.6304 | 0.79 | | 0.14 | 9.0 | 1017 | 0.6143 | 0.82 | | 0.0891 | 10.0 | 1130 | 0.6070 | 0.81 | ### Framework versions - Transformers 4.57.1 - Pytorch 2.8.0+cu126 - Datasets 2.21.0 - Tokenizers 0.22.1