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: 1.0312
- 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-05
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.218         | 1.0   | 899  | 1.4800          | 0.54     |
| 1.2067        | 2.0   | 1798 | 1.6373          | 0.63     |
| 0.0462        | 3.0   | 2697 | 0.9210          | 0.73     |
| 0.0276        | 4.0   | 3596 | 0.9785          | 0.82     |
| 0.0291        | 5.0   | 4495 | 1.2520          | 0.77     |
| 0.0036        | 6.0   | 5394 | 1.1841          | 0.81     |
| 0.0004        | 7.0   | 6293 | 1.1607          | 0.82     |
| 0.0002        | 8.0   | 7192 | 1.2134          | 0.79     |
| 0.0001        | 9.0   | 8091 | 0.9547          | 0.85     |
| 0.0002        | 10.0  | 8990 | 1.0312          | 0.84     |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
