---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ntu-spml/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: 0.87
---

<!-- 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. -->

# ntu-spml/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: 0.7428
- Accuracy: 0.87

## 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: 3.992986714871485e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9807885777224674,0.996064720140604) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 509
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 57   | 2.2832          | 0.3      |
| No log        | 2.0   | 114  | 2.2273          | 0.28     |
| No log        | 3.0   | 171  | 2.0861          | 0.46     |
| No log        | 4.0   | 228  | 1.8473          | 0.5      |
| No log        | 5.0   | 285  | 1.5146          | 0.6      |
| No log        | 6.0   | 342  | 1.2140          | 0.69     |
| No log        | 7.0   | 399  | 0.9856          | 0.74     |
| No log        | 8.0   | 456  | 0.8056          | 0.79     |
| 1.6591        | 9.0   | 513  | 0.7135          | 0.8      |
| 1.6591        | 10.0  | 570  | 0.7642          | 0.75     |
| 1.6591        | 11.0  | 627  | 0.6344          | 0.79     |
| 1.6591        | 12.0  | 684  | 0.5982          | 0.83     |
| 1.6591        | 13.0  | 741  | 0.5369          | 0.86     |
| 1.6591        | 14.0  | 798  | 0.7501          | 0.79     |
| 1.6591        | 15.0  | 855  | 0.7493          | 0.78     |
| 1.6591        | 16.0  | 912  | 0.6891          | 0.83     |
| 1.6591        | 17.0  | 969  | 0.7492          | 0.8      |
| 0.2402        | 18.0  | 1026 | 0.6663          | 0.88     |
| 0.2402        | 19.0  | 1083 | 0.5750          | 0.89     |
| 0.2402        | 20.0  | 1140 | 0.8215          | 0.81     |
| 0.2402        | 21.0  | 1197 | 0.7435          | 0.79     |
| 0.2402        | 22.0  | 1254 | 0.8305          | 0.86     |
| 0.2402        | 23.0  | 1311 | 0.7636          | 0.83     |
| 0.2402        | 24.0  | 1368 | 0.9786          | 0.77     |
| 0.2402        | 25.0  | 1425 | 0.7082          | 0.88     |
| 0.2402        | 26.0  | 1482 | 0.7698          | 0.85     |
| 0.0206        | 27.0  | 1539 | 0.7360          | 0.87     |
| 0.0206        | 28.0  | 1596 | 0.8575          | 0.84     |
| 0.0206        | 29.0  | 1653 | 0.7428          | 0.87     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.13.2.dev1
- Tokenizers 0.13.3