Distilhubert Finetuned Gtzan

--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - sanchit-gandhi/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: sanchit-gandhi/gtzan metrics: - name: Accuracy type: accuracy value: 0.76 --- <!-- 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: 0.7623 - Accuracy: 0.76 ## 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_steps: 100 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9318 | 1.0 | 100 | 1.9005 | 0.41 | | 1.3362 | 2.0 | 200 | 1.3615 | 0.64 | | 1.0278 | 3.0 | 300 | 1.1866 | 0.665 | | 0.6667 | 4.0 | 400 | 1.0135 | 0.675 | | 0.4944 | 5.0 | 500 | 0.9487 | 0.7 | | 0.4093 | 6.0 | 600 | 0.8547 | 0.745 | | 0.3764 | 7.0 | 700 | 0.7908 | 0.745 | | 0.3551 | 8.0 | 800 | 0.7326 | 0.79 | | 0.2107 | 9.0 | 900 | 0.7463 | 0.765 | | 0.1919 | 10.0 | 1000 | 0.7623 | 0.76 | ### Framework versions - Transformers 5.0.0 - Pytorch 2.9.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.2

Source context: 6 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo rafaelaraujo13/distilhubert-finetuned-gtzan