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: 0.88
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:
- Accuracy: 0.88
- Loss: 0.4331
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 2.2693 | 0.99 | 28 | 0.31 | 2.2480 |
| 1.9782 | 1.98 | 56 | 0.45 | 1.8990 |
| 1.6438 | 2.97 | 84 | 0.62 | 1.5180 |
| 1.3307 | 4.0 | 113 | 0.73 | 1.2206 |
| 1.133 | 4.99 | 141 | 0.76 | 0.9961 |
| 0.9384 | 5.98 | 169 | 0.78 | 0.8889 |
| 0.8668 | 6.97 | 197 | 0.79 | 0.7543 |
| 0.674 | 8.0 | 226 | 0.79 | 0.7433 |
| 0.5997 | 8.99 | 254 | 0.83 | 0.6194 |
| 0.5195 | 9.98 | 282 | 0.91 | 0.5685 |
| 0.401 | 10.97 | 310 | 0.91 | 0.5144 |
| 0.3151 | 12.0 | 339 | 0.87 | 0.4775 |
| 0.2653 | 12.99 | 367 | 0.88 | 0.4984 |
| 0.2182 | 13.98 | 395 | 0.88 | 0.4337 |
| 0.2036 | 14.97 | 423 | 0.89 | 0.4657 |
| 0.1925 | 16.0 | 452 | 0.89 | 0.4222 |
| 0.1807 | 16.99 | 480 | 0.87 | 0.4512 |
| 0.1626 | 17.98 | 508 | 0.88 | 0.4247 |
| 0.1388 | 18.97 | 536 | 0.88 | 0.4324 |
| 0.1718 | 19.82 | 560 | 0.88 | 0.4331 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0