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---
license: apache-2.0
base_model: weiren119/distilhubert-finetuned-finetuned-gtzan
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: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- 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 [weiren119/distilhubert-finetuned-finetuned-gtzan](https://huggingface.co/weiren119/distilhubert-finetuned-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9411
- Accuracy: 0.88
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0012 | 1.0 | 57 | 0.9231 | 0.86 |
| 0.0009 | 2.0 | 114 | 0.9304 | 0.88 |
| 0.0007 | 3.0 | 171 | 0.9359 | 0.87 |
| 0.0008 | 4.0 | 228 | 1.0345 | 0.85 |
| 0.0007 | 5.0 | 285 | 0.9492 | 0.87 |
| 0.0139 | 6.0 | 342 | 0.9883 | 0.86 |
| 0.0409 | 7.0 | 399 | 0.9377 | 0.88 |
| 0.0005 | 8.0 | 456 | 0.9460 | 0.88 |
| 0.0005 | 9.0 | 513 | 0.9462 | 0.88 |
| 0.0005 | 10.0 | 570 | 0.9411 | 0.88 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|