---
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:
- Accuracy: 0.88
- Loss: 0.4331

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.2693        | 0.99  | 28   | 0.31     | 2.2480          |
| 1.9782        | 1.98  | 56   | 0.45     | 1.8990          |
| 1.6438        | 2.97  | 84   | 0.62     | 1.5180          |
| 1.3307        | 4.0   | 113  | 0.73     | 1.2206          |
| 1.133         | 4.99  | 141  | 0.76     | 0.9961          |
| 0.9384        | 5.98  | 169  | 0.78     | 0.8889          |
| 0.8668        | 6.97  | 197  | 0.79     | 0.7543          |
| 0.674         | 8.0   | 226  | 0.79     | 0.7433          |
| 0.5997        | 8.99  | 254  | 0.83     | 0.6194          |
| 0.5195        | 9.98  | 282  | 0.91     | 0.5685          |
| 0.401         | 10.97 | 310  | 0.91     | 0.5144          |
| 0.3151        | 12.0  | 339  | 0.87     | 0.4775          |
| 0.2653        | 12.99 | 367  | 0.88     | 0.4984          |
| 0.2182        | 13.98 | 395  | 0.88     | 0.4337          |
| 0.2036        | 14.97 | 423  | 0.89     | 0.4657          |
| 0.1925        | 16.0  | 452  | 0.89     | 0.4222          |
| 0.1807        | 16.99 | 480  | 0.87     | 0.4512          |
| 0.1626        | 17.98 | 508  | 0.88     | 0.4247          |
| 0.1388        | 18.97 | 536  | 0.88     | 0.4324          |
| 0.1718        | 19.82 | 560  | 0.88     | 0.4331          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0