---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-v2
  results: []
datasets:
- marsyas/gtzan
---

<!-- 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. -->

# distilhubert-finetuned-gtzan-v2

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: 0.5528
- Accuracy: 0.87

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2975        | 1.0   | 14   | 2.2790          | 0.26     |
| 2.255         | 1.99  | 28   | 2.1863          | 0.39     |
| 2.0948        | 2.99  | 42   | 1.9637          | 0.43     |
| 1.847         | 3.98  | 56   | 1.7093          | 0.54     |
| 1.5798        | 4.98  | 70   | 1.5095          | 0.62     |
| 1.4674        | 5.97  | 84   | 1.3173          | 0.67     |
| 1.2969        | 6.97  | 98   | 1.1894          | 0.72     |
| 1.1472        | 7.96  | 112  | 1.0415          | 0.77     |
| 0.9815        | 8.96  | 126  | 1.0004          | 0.74     |
| 0.8838        | 9.96  | 140  | 0.8808          | 0.78     |
| 0.8294        | 10.95 | 154  | 0.8551          | 0.78     |
| 0.768         | 11.95 | 168  | 0.7939          | 0.79     |
| 0.6499        | 12.94 | 182  | 0.7467          | 0.81     |
| 0.6014        | 13.94 | 196  | 0.6995          | 0.82     |
| 0.5296        | 14.93 | 210  | 0.7152          | 0.79     |
| 0.4478        | 16.0  | 225  | 0.6561          | 0.83     |
| 0.4082        | 17.0  | 239  | 0.6399          | 0.84     |
| 0.374         | 17.99 | 253  | 0.6217          | 0.86     |
| 0.3282        | 18.99 | 267  | 0.5991          | 0.85     |
| 0.28          | 19.98 | 281  | 0.6043          | 0.84     |
| 0.2754        | 20.98 | 295  | 0.5831          | 0.87     |
| 0.2409        | 21.97 | 309  | 0.5680          | 0.85     |
| 0.2172        | 22.97 | 323  | 0.5729          | 0.85     |
| 0.1855        | 23.96 | 337  | 0.5645          | 0.86     |
| 0.1729        | 24.96 | 351  | 0.5576          | 0.86     |
| 0.161         | 25.96 | 365  | 0.5378          | 0.86     |
| 0.1586        | 26.95 | 379  | 0.5662          | 0.86     |
| 0.1452        | 27.95 | 393  | 0.5575          | 0.87     |
| 0.1444        | 28.94 | 407  | 0.5491          | 0.86     |
| 0.1343        | 29.87 | 420  | 0.5528          | 0.86     |


### Framework versions

- Transformers 4.39.2
- Pytorch 1.13.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1