transformers tensorboard safetensors hubert audio-classification generated_from_trainer dataset:marsyas/gtzan base_model:ntu-spml/distilhubert license:apache-2.0 model-index endpoints_compatible region:us
---
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
---

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# 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: 0.5771
- 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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1148        | 0.11  | 5    | 0.5865          | 0.83     |
| 0.1411        | 0.22  | 10   | 0.5951          | 0.83     |
| 0.1014        | 0.33  | 15   | 0.5964          | 0.83     |
| 0.085         | 0.44  | 20   | 0.5901          | 0.83     |
| 0.1362        | 0.56  | 25   | 0.5894          | 0.82     |
| 0.0917        | 0.67  | 30   | 0.5862          | 0.83     |
| 0.097         | 0.78  | 35   | 0.5759          | 0.84     |
| 0.1206        | 0.89  | 40   | 0.5701          | 0.84     |
| 0.0909        | 1.0   | 45   | 0.5649          | 0.84     |
| 0.1269        | 1.11  | 50   | 0.5674          | 0.84     |
| 0.1117        | 1.22  | 55   | 0.5714          | 0.84     |
| 0.0791        | 1.33  | 60   | 0.5730          | 0.86     |
| 0.1016        | 1.44  | 65   | 0.5745          | 0.84     |
| 0.0712        | 1.56  | 70   | 0.5744          | 0.85     |
| 0.1212        | 1.67  | 75   | 0.5773          | 0.85     |
| 0.0724        | 1.78  | 80   | 0.5782          | 0.85     |
| 0.0831        | 1.89  | 85   | 0.5777          | 0.85     |
| 0.1429        | 2.0   | 90   | 0.5771          | 0.84     |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
