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: 0.82
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: 0.9462
- Accuracy: 0.82
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: 0.0001
- 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 |
|---|---|---|---|---|
| 0.5455 | 1.0 | 57 | 0.7144 | 0.76 |
| 0.5342 | 2.0 | 114 | 0.8039 | 0.75 |
| 0.1636 | 3.0 | 171 | 0.6388 | 0.83 |
| 0.1868 | 4.0 | 228 | 0.6027 | 0.81 |
| 0.0643 | 5.0 | 285 | 0.6728 | 0.83 |
| 0.0418 | 6.0 | 342 | 0.6726 | 0.82 |
| 0.0925 | 7.0 | 399 | 0.9795 | 0.81 |
| 0.0047 | 8.0 | 456 | 1.0072 | 0.82 |
| 0.0296 | 9.0 | 513 | 0.9450 | 0.82 |
| 0.0031 | 10.0 | 570 | 0.9462 | 0.82 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3