---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-(gaurav)-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: 0.82
---

<!-- 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-(gaurav)-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: 0.5492
- 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: 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9927        | 1.0   | 113  | 1.9567          | 0.44     |
| 1.2473        | 2.0   | 226  | 1.3288          | 0.56     |
| 1.1117        | 3.0   | 339  | 0.9260          | 0.75     |
| 0.6962        | 4.0   | 452  | 0.9979          | 0.71     |
| 0.6209        | 5.0   | 565  | 0.6911          | 0.77     |
| 0.4144        | 6.0   | 678  | 0.5780          | 0.83     |
| 0.3158        | 7.0   | 791  | 0.6068          | 0.8      |
| 0.1404        | 8.0   | 904  | 0.5806          | 0.84     |
| 0.1242        | 9.0   | 1017 | 0.5406          | 0.82     |
| 0.1086        | 10.0  | 1130 | 0.5492          | 0.82     |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1