metadata
library_name: transformers
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: 0.8333333333333334
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: 0.7729
- Accuracy: 0.8333
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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.188 | 1.0 | 35 | 2.1681 | 0.2692 |
| 1.887 | 2.0 | 70 | 1.8252 | 0.5769 |
| 1.5321 | 3.0 | 105 | 1.4375 | 0.5385 |
| 1.0946 | 4.0 | 140 | 1.2295 | 0.6282 |
| 0.9091 | 5.0 | 175 | 1.0390 | 0.6923 |
| 0.6839 | 6.0 | 210 | 0.9047 | 0.7821 |
| 0.5769 | 7.0 | 245 | 0.8309 | 0.7308 |
| 0.4118 | 8.0 | 280 | 0.9522 | 0.6538 |
| 0.3767 | 9.0 | 315 | 0.8164 | 0.7308 |
| 0.2247 | 10.0 | 350 | 0.6987 | 0.8205 |
| 0.1392 | 11.0 | 385 | 0.7565 | 0.7692 |
| 0.0886 | 12.0 | 420 | 0.7082 | 0.8205 |
| 0.0583 | 13.0 | 455 | 0.7529 | 0.8205 |
| 0.0383 | 14.0 | 490 | 0.7678 | 0.7949 |
| 0.0345 | 15.0 | 525 | 0.7480 | 0.8333 |
| 0.0269 | 16.0 | 560 | 0.7542 | 0.8333 |
| 0.0246 | 17.0 | 595 | 0.7550 | 0.8205 |
| 0.0233 | 18.0 | 630 | 0.7725 | 0.8333 |
| 0.0225 | 19.0 | 665 | 0.7701 | 0.8333 |
| 0.0225 | 20.0 | 700 | 0.7729 | 0.8333 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0