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: 0.66
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: 1.6170
- Accuracy: 0.66
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|---|---|---|---|---|
| 2.2999 | 0.97 | 7 | 2.2700 | 0.28 |
| 2.2713 | 1.93 | 14 | 2.1859 | 0.36 |
| 2.1478 | 2.9 | 21 | 2.0656 | 0.47 |
| 2.0863 | 4.0 | 29 | 1.9387 | 0.53 |
| 1.9229 | 4.97 | 36 | 1.8303 | 0.62 |
| 1.8399 | 5.93 | 43 | 1.7453 | 0.59 |
| 1.7467 | 6.9 | 50 | 1.6898 | 0.58 |
| 1.7223 | 8.0 | 58 | 1.6360 | 0.6 |
| 1.6716 | 8.97 | 65 | 1.6243 | 0.65 |
| 1.6509 | 9.66 | 70 | 1.6170 | 0.66 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.0+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3