transformers tensorboard safetensors hubert audio-classification generated_from_trainer dataset:marsyas/gtzan base_model:chaouch/distilhubert-finetuned-gtzan license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: chaouch/distilhubert-finetuned-gtzan
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-finetuned-gtzan2
  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: 0.85
---

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# distilhubert-finetuned-gtzan-finetuned-gtzan2

This model is a fine-tuned version of [chaouch/distilhubert-finetuned-gtzan](https://huggingface.co/chaouch/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7901
- Accuracy: 0.85

## 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: 16
- eval_batch_size: 16
- 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0776        | 1.0   | 57   | 0.7597          | 0.81     |
| 0.0272        | 2.0   | 114  | 0.6991          | 0.84     |
| 0.0132        | 3.0   | 171  | 0.7621          | 0.85     |
| 0.0089        | 4.0   | 228  | 0.7702          | 0.85     |
| 0.0071        | 5.0   | 285  | 0.7901          | 0.85     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
