STRIX Heavy Model

--- library_name: transformers license: bsd-3-clause base_model: MIT/ast-finetuned-audioset-10-10-0.4593 tags: - generated_from_trainer metrics: - accuracy model-index: - name: classificateur-audio-distilhubert results: [] --- <!-- 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. --> # classificateur-audio-distilhubert This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1506 - Accuracy: 0.9644 ## 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: 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: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1129 | 1.0 | 540 | 0.2396 | 0.9375 | | 0.0178 | 2.0 | 1080 | 0.1506 | 0.9644 | ### Framework versions - Transformers 5.10.2 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2

Source context: 5 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo Newton2676/STRIX_heavy_model

Source context: 6 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo Newton2676/STRIX_heavy_model