---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
base_model: facebook/hubert-large-ll60k
model-index:
- name: hubert-large-ll60k-finetuned-gtzan
  results: []
---

<!-- 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. -->

# hubert-large-ll60k-finetuned-gtzan

This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9220
- Accuracy: 0.73

## 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: 7e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2456        | 1.0   | 56   | 2.2312          | 0.34     |
| 2.059         | 1.99  | 112  | 1.9662          | 0.32     |
| 1.8574        | 2.99  | 168  | 1.6258          | 0.5      |
| 1.4447        | 4.0   | 225  | 1.4547          | 0.59     |
| 1.4224        | 5.0   | 281  | 1.2372          | 0.65     |
| 1.2131        | 5.99  | 337  | 1.0879          | 0.67     |
| 1.1151        | 6.99  | 393  | 1.0599          | 0.69     |
| 0.9471        | 8.0   | 450  | 1.0339          | 0.68     |
| 1.0319        | 9.0   | 506  | 0.9568          | 0.71     |
| 0.9313        | 9.96  | 560  | 0.9220          | 0.73     |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3