---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-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. -->

# 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: 1.2454
- Accuracy: 0.82

## 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: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2107        | 1.0   | 112  | 2.2411          | 0.31     |
| 2.0193        | 2.0   | 225  | 1.9900          | 0.53     |
| 1.7491        | 3.0   | 337  | 1.6436          | 0.59     |
| 1.5096        | 4.0   | 450  | 1.3625          | 0.63     |
| 0.9801        | 5.0   | 562  | 1.0769          | 0.75     |
| 0.8603        | 6.0   | 675  | 0.9399          | 0.78     |
| 0.5573        | 7.0   | 787  | 0.8290          | 0.77     |
| 0.5776        | 8.0   | 900  | 0.6834          | 0.82     |
| 0.4687        | 9.0   | 1012 | 0.6522          | 0.82     |
| 0.3513        | 10.0  | 1125 | 0.6564          | 0.82     |
| 0.1691        | 11.0  | 1237 | 0.6628          | 0.84     |
| 0.0384        | 12.0  | 1350 | 0.8602          | 0.81     |
| 0.0218        | 13.0  | 1462 | 0.8367          | 0.85     |
| 0.0057        | 14.0  | 1575 | 0.9951          | 0.83     |
| 0.0041        | 15.0  | 1687 | 1.0021          | 0.84     |
| 0.0027        | 16.0  | 1800 | 1.0215          | 0.82     |
| 0.0021        | 17.0  | 1912 | 0.9737          | 0.83     |
| 0.0017        | 18.0  | 2025 | 1.0321          | 0.85     |
| 0.0015        | 19.0  | 2137 | 0.9519          | 0.81     |
| 0.0013        | 20.0  | 2250 | 0.9298          | 0.82     |
| 0.0011        | 21.0  | 2362 | 0.9627          | 0.83     |
| 0.001         | 22.0  | 2475 | 1.1373          | 0.82     |
| 0.0009        | 23.0  | 2587 | 1.0855          | 0.83     |
| 0.0008        | 24.0  | 2700 | 0.9979          | 0.81     |
| 0.0008        | 25.0  | 2812 | 1.0956          | 0.82     |
| 0.0009        | 26.0  | 2925 | 0.9861          | 0.82     |
| 0.0007        | 27.0  | 3037 | 1.1387          | 0.83     |
| 0.0006        | 28.0  | 3150 | 1.1965          | 0.83     |
| 0.0006        | 29.0  | 3262 | 1.1527          | 0.81     |
| 0.0007        | 30.0  | 3375 | 1.0609          | 0.82     |
| 0.0006        | 31.0  | 3487 | 1.1770          | 0.81     |
| 0.0801        | 32.0  | 3600 | 1.2290          | 0.82     |
| 0.0005        | 33.0  | 3712 | 1.1785          | 0.83     |
| 0.0005        | 34.0  | 3825 | 1.2154          | 0.83     |
| 0.0004        | 35.0  | 3937 | 1.2250          | 0.83     |
| 0.0004        | 36.0  | 4050 | 1.2280          | 0.82     |
| 0.0004        | 37.0  | 4162 | 1.2364          | 0.83     |
| 0.0004        | 38.0  | 4275 | 1.2379          | 0.82     |
| 0.0004        | 39.0  | 4387 | 1.2483          | 0.83     |
| 0.0004        | 39.82 | 4480 | 1.2454          | 0.82     |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2