metadata
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: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.83
distilhubert-finetuned-gtzan
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.5991
- Accuracy: 0.83
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: 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 |
|---|---|---|---|---|
| 2.1211 | 1.0 | 57 | 1.9967 | 0.4 |
| 1.6311 | 2.0 | 114 | 1.5599 | 0.58 |
| 1.2082 | 3.0 | 171 | 1.2194 | 0.72 |
| 1.1853 | 4.0 | 228 | 1.0276 | 0.75 |
| 0.7278 | 5.0 | 285 | 0.9232 | 0.78 |
| 0.6999 | 6.0 | 342 | 0.7392 | 0.82 |
| 0.4983 | 7.0 | 399 | 0.6779 | 0.84 |
| 0.5142 | 8.0 | 456 | 0.6483 | 0.83 |
| 0.417 | 9.0 | 513 | 0.6554 | 0.82 |
| 0.3725 | 10.0 | 570 | 0.5991 | 0.83 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3