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---
library_name: transformers
license: apache-2.0
base_model: sveyek/distilhubert-finetuned-gtzan-se-finetuned-gtzan-se-2
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-se-finetuned-gtzan-se-2-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: 0.81
---
<!-- 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-se-finetuned-gtzan-se-2-finetuned-gtzan
This model is a fine-tuned version of [sveyek/distilhubert-finetuned-gtzan-se-finetuned-gtzan-se-2](https://huggingface.co/sveyek/distilhubert-finetuned-gtzan-se-finetuned-gtzan-se-2) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2073
- Accuracy: 0.81
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0759 | 0.9956 | 112 | 0.9223 | 0.84 |
| 0.0011 | 2.0 | 225 | 1.1652 | 0.82 |
| 0.0004 | 2.9867 | 336 | 1.2073 | 0.81 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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