metadata
license: apache-2.0
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: 0.905
distilhubert-finetuned-gtzan
This model is a fine-tuned version of NemesisAlm/distilhubert-finetuned-gtzan on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.7322
- Accuracy: 0.905
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|
| 0.0002 | 1.0 | 100 | 0.5783 | 0.915 |
| 0.1984 | 2.0 | 200 | 0.7051 | 0.91 |
| 0.0518 | 3.0 | 300 | 1.0287 | 0.865 |
| 0.0039 | 4.0 | 400 | 0.7660 | 0.895 |
| 0.0001 | 5.0 | 500 | 0.7513 | 0.91 |
| 0.0001 | 6.0 | 600 | 0.7757 | 0.9 |
| 0.0002 | 7.0 | 700 | 0.9340 | 0.87 |
| 0.0001 | 8.0 | 800 | 0.7237 | 0.9 |
| 0.0001 | 9.0 | 900 | 0.7298 | 0.905 |
| 0.0001 | 10.0 | 1000 | 0.7322 | 0.905 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3