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---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: pratik33/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: 0.7625
---

<!-- 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. -->

# pratik33/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: 0.7774
- Accuracy: 0.7625

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0699        | 1.0   | 75   | 2.0157          | 0.375    |
| 1.5657        | 2.0   | 150  | 1.4965          | 0.5775   |
| 1.3258        | 3.0   | 225  | 1.2250          | 0.6325   |
| 0.9701        | 4.0   | 300  | 1.0614          | 0.7175   |
| 0.9475        | 5.0   | 375  | 0.9632          | 0.725    |
| 0.8134        | 6.0   | 450  | 0.8347          | 0.7725   |
| 0.8038        | 7.0   | 525  | 0.8290          | 0.7575   |
| 0.3698        | 8.0   | 600  | 0.7886          | 0.775    |
| 0.4005        | 9.0   | 675  | 0.8095          | 0.7625   |
| 0.3392        | 10.0  | 750  | 0.7774          | 0.7625   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3