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: 0.81
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: 0.6443
- 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: 7
- eval_batch_size: 7
- 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 |
|---|---|---|---|---|
| 1.9572 | 1.0 | 129 | 1.7251 | 0.52 |
| 1.1852 | 2.0 | 258 | 1.1422 | 0.71 |
| 1.1112 | 3.0 | 387 | 0.9251 | 0.72 |
| 0.5134 | 4.0 | 516 | 0.7149 | 0.79 |
| 0.3248 | 5.0 | 645 | 0.7624 | 0.76 |
| 0.332 | 6.0 | 774 | 0.7291 | 0.78 |
| 0.1595 | 7.0 | 903 | 0.6012 | 0.81 |
| 0.1878 | 8.0 | 1032 | 0.5808 | 0.85 |
| 0.0617 | 9.0 | 1161 | 0.6518 | 0.82 |
| 0.0323 | 10.0 | 1290 | 0.6443 | 0.81 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3