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: 0.88 --- <!-- 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: 0.80 - Accuracy: 0.89 - Macro F1: 0.8957 ## 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: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH 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: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 2.2641 | 1.0 | 450 | 2.2228 | 0.45 | 0.3545 | | 1.8857 | 2.0 | 900 | 1.8527 | 0.56 | 0.4640 | | 1.6008 | 3.0 | 1350 | 1.4108 | 0.68 | 0.6543 | | 1.1742 | 4.0 | 1800 | 1.1191 | 0.72 | 0.6910 | | 0.9257 | 5.0 | 2250 | 0.8738 | 0.81 | 0.8026 | | 0.7766 | 6.0 | 2700 | 0.6843 | 0.82 | 0.8216 | | 0.4557 | 7.0 | 3150 | 0.6263 | 0.82 | 0.8170 | | 0.3278 | 8.0 | 3600 | 0.5555 | 0.84 | 0.8285 | | 0.3262 | 9.0 | 4050 | 0.5471 | 0.87 | 0.8641 | | 0.2547 | 10.0 | 4500 | 0.7685 | 0.81 | 0.8043 | | 0.2713 | 11.0 | 4950 | 0.6107 | 0.89 | 0.8782 | | 0.1555 | 12.0 | 5400 | 0.6796 | 0.88 | 0.8767 | | 0.1983 | 13.0 | 5850 | 0.8251 | 0.86 | 0.8466 | | 0.1146 | 14.0 | 6300 | 0.8332 | 0...