MERT v1 330M Finetuned Gtzan

--- library_name: transformers license: cc-by-nc-4.0 base_model: m-a-p/MERT-v1-330M tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: m-a-p/MERT-v1-330M-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan metrics: - name: Accuracy type: accuracy value: 0.93 --- <!-- 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. --> # m-a-p/MERT-v1-330M-finetuned-gtzan This model is a fine-tuned version of [m-a-p/MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.4795 - Accuracy: 0.93 ## 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: 16 - eval_batch_size: 16 - 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: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.125 | 1.0 | 57 | 2.0967 | 0.46 | | 1.1862 | 2.0 | 114 | 1.0739 | 0.73 | | 0.7055 | 3.0 | 171 | 0.7744 | 0.81 | | 0.445 | 4.0 | 228 | 0.5283 | 0.86 | | 0.2788 | 5.0 | 285 | 0.4848 | 0.89 | | 0.0948 | 6.0 | 342 | 0.5192 | 0.89 | | 0.0709 | 7.0 | 399 | 0.4638 | 0.9 | | 0.0818 | 8.0 | 456 | 0.4345 | 0.91 | | 0.0835 | 9.0 | 513 | 0.4638 | 0.92 | | 0.0063 | 10.0 | 570 | 0.4175 | 0.94 | | 0.0052 | 11.0 | 627 | 0.4807 | 0.93 | | 0.0055 | 12.0 | 684 | 0.4814 | 0.93 | | 0.0235 | 13.0 | 741 | 0.4796 | 0.93 | | 0.0043 | 14.0 | 798 | 0.4793 | 0.93 | | 0.0044 | 15.0 | 855 | 0.4795 | 0.93 | ### Framework versions - Transformers 4.57.3 - Pytorch 2.9.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.1