---
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: 0.84
---

<!-- 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: 0.9085
- Accuracy: 0.84

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3000

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.0825        | 0.88  | 100  | 0.47     | 1.8392          |
| 1.4043        | 1.77  | 200  | 0.67     | 1.2675          |
| 1.0686        | 2.65  | 300  | 0.71     | 1.0186          |
| 0.8037        | 3.54  | 400  | 0.74     | 0.9198          |
| 0.6215        | 4.42  | 500  | 0.78     | 0.7636          |
| 0.5106        | 5.31  | 600  | 0.76     | 0.7937          |
| 0.3844        | 6.19  | 700  | 0.78     | 0.6909          |
| 0.3043        | 7.08  | 800  | 0.77     | 0.7279          |
| 0.2453        | 7.96  | 900  | 0.82     | 0.6447          |
| 0.211         | 8.85  | 1000 | 0.84     | 0.6404          |
| 0.2268        | 9.73  | 1100 | 0.77     | 0.7198          |
| 0.1565        | 10.62 | 1200 | 0.83     | 0.6704          |
| 0.0694        | 11.5  | 1300 | 0.83     | 0.8017          |
| 0.0568        | 12.39 | 1400 | 0.8      | 0.7841          |
| 0.0441        | 13.27 | 1500 | 0.81     | 0.7757          |
| 0.0302        | 14.16 | 1600 | 0.84     | 0.7819          |
| 0.0116        | 15.04 | 1700 | 0.83     | 0.7949          |
| 0.0289        | 15.93 | 1800 | 0.85     | 0.8057          |
| 0.0115        | 16.81 | 1900 | 0.83     | 0.8271          |
| 0.0081        | 17.7  | 2000 | 0.86     | 0.8005          |
| 0.0124        | 18.58 | 2100 | 0.8      | 0.8927          |
| 0.0219        | 19.47 | 2200 | 0.85     | 0.8126          |
| 0.0161        | 20.35 | 2300 | 0.85     | 0.8464          |
| 0.0157        | 21.24 | 2400 | 0.86     | 0.8459          |
| 0.0039        | 22.12 | 2500 | 0.8      | 1.0282          |
| 0.0145        | 23.01 | 2600 | 0.9218   | 0.84            |
| 0.0149        | 23.89 | 2700 | 0.9085   | 0.84            |


### Framework versions

- Transformers 4.32.0
- Pytorch 1.12.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3