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---
library_name: transformers
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: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.86
---
<!-- 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: 1.1749
- Accuracy: 0.86
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0517 | 1.0 | 225 | 2.0004 | 0.47 |
| 1.3283 | 2.0 | 450 | 1.3458 | 0.57 |
| 0.729 | 3.0 | 675 | 0.8563 | 0.76 |
| 0.4007 | 4.0 | 900 | 0.6748 | 0.8 |
| 0.3923 | 5.0 | 1125 | 0.7340 | 0.78 |
| 0.2193 | 6.0 | 1350 | 0.8712 | 0.76 |
| 0.2383 | 7.0 | 1575 | 0.7414 | 0.79 |
| 0.3 | 8.0 | 1800 | 0.7387 | 0.86 |
| 0.006 | 9.0 | 2025 | 0.9203 | 0.85 |
| 0.002 | 10.0 | 2250 | 0.8956 | 0.85 |
| 0.0014 | 11.0 | 2475 | 0.9831 | 0.86 |
| 0.001 | 12.0 | 2700 | 0.9406 | 0.86 |
| 0.0009 | 13.0 | 2925 | 1.0288 | 0.86 |
| 0.0007 | 14.0 | 3150 | 1.0172 | 0.86 |
| 0.0007 | 15.0 | 3375 | 0.9912 | 0.89 |
| 0.0005 | 16.0 | 3600 | 1.0282 | 0.86 |
| 0.0006 | 17.0 | 3825 | 1.3495 | 0.83 |
| 0.2453 | 18.0 | 4050 | 1.0340 | 0.87 |
| 0.0004 | 19.0 | 4275 | 1.1048 | 0.86 |
| 0.0004 | 20.0 | 4500 | 1.3051 | 0.85 |
| 0.0003 | 21.0 | 4725 | 1.2280 | 0.85 |
| 0.0003 | 22.0 | 4950 | 1.2530 | 0.85 |
| 0.0003 | 23.0 | 5175 | 1.1992 | 0.85 |
| 0.0003 | 24.0 | 5400 | 1.1881 | 0.85 |
| 0.0003 | 25.0 | 5625 | 1.1749 | 0.86 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
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