---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- calixtemayoraz/FMA-music-dataset
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-FMA-music
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: FMA-music-dataset
      type: calixtemayoraz/FMA-music-dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.45754716981132076
---

<!-- 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-FMA-music

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the FMA-music-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8929
- Accuracy: 0.4575

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0384        | 1.0   | 238  | 2.2583          | 0.2358   |
| 1.9204        | 2.0   | 476  | 2.1387          | 0.2972   |
| 1.6768        | 3.0   | 714  | 1.9356          | 0.3774   |
| 1.7940        | 4.0   | 952  | 1.8584          | 0.3868   |
| 1.5281        | 5.0   | 1190 | 1.7814          | 0.4434   |
| 1.1608        | 6.0   | 1428 | 1.8745          | 0.4151   |
| 1.2103        | 7.0   | 1666 | 1.8066          | 0.4434   |
| 0.8522        | 8.0   | 1904 | 1.8688          | 0.4434   |
| 0.7455        | 9.0   | 2142 | 1.8818          | 0.4528   |
| 0.5444        | 10.0  | 2380 | 1.8929          | 0.4575   |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2