---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-2.5E-5rate
  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: 0.82
---

<!-- 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-2.5E-5rate

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: 0.5939
- Accuracy: 0.82

## 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: 2.5e-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
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2487        | 1.0   | 113  | 2.1744          | 0.41     |
| 1.7534        | 2.0   | 226  | 1.6280          | 0.65     |
| 1.4458        | 3.0   | 339  | 1.2993          | 0.7      |
| 1.2271        | 4.0   | 452  | 1.1119          | 0.72     |
| 1.115         | 5.0   | 565  | 1.0119          | 0.75     |
| 0.834         | 6.0   | 678  | 0.8967          | 0.77     |
| 1.0247        | 7.0   | 791  | 0.8154          | 0.78     |
| 0.6211        | 8.0   | 904  | 0.7035          | 0.81     |
| 0.7136        | 9.0   | 1017 | 0.6755          | 0.8      |
| 0.464         | 10.0  | 1130 | 0.6808          | 0.84     |
| 0.2952        | 11.0  | 1243 | 0.6245          | 0.8      |
| 0.3117        | 12.0  | 1356 | 0.6150          | 0.84     |
| 0.24          | 13.0  | 1469 | 0.6000          | 0.82     |
| 0.2554        | 14.0  | 1582 | 0.5952          | 0.83     |
| 0.2452        | 15.0  | 1695 | 0.5939          | 0.82     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3