metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: music-genre-classifer-20-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
music-genre-classifer-20-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: 0.8224
- 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.201 | 1.0 | 113 | 2.1256 | 0.39 |
| 1.6789 | 2.0 | 226 | 1.6543 | 0.59 |
| 1.5602 | 3.0 | 339 | 1.3917 | 0.64 |
| 1.1966 | 4.0 | 452 | 1.1946 | 0.67 |
| 1.1131 | 5.0 | 565 | 1.0492 | 0.77 |
| 1.0258 | 6.0 | 678 | 0.9712 | 0.76 |
| 0.988 | 7.0 | 791 | 0.9160 | 0.76 |
| 0.7303 | 8.0 | 904 | 0.8704 | 0.8 |
| 0.8036 | 9.0 | 1017 | 0.8425 | 0.8 |
| 0.742 | 10.0 | 1130 | 0.8224 | 0.81 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2