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: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8581829692940804
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: 1.5627
- Accuracy: 0.8582
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
- 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 |
|---|---|---|---|---|
| 0.4173 | 1.0 | 7108 | 0.5416 | 0.8343 |
| 0.235 | 2.0 | 14216 | 0.4663 | 0.8251 |
| 0.1549 | 3.0 | 21324 | 0.5940 | 0.8325 |
| 0.2558 | 4.0 | 28432 | 0.6608 | 0.8531 |
| 0.2991 | 5.0 | 35540 | 0.9088 | 0.8305 |
| 0.4773 | 6.0 | 42648 | 0.9120 | 0.8390 |
| 0.5235 | 7.0 | 49756 | 0.9285 | 0.8455 |
| 0.0004 | 8.0 | 56864 | 1.0259 | 0.8492 |
| 0.1918 | 9.0 | 63972 | 1.2874 | 0.8411 |
| 0.0002 | 10.0 | 71080 | 1.1114 | 0.8476 |
| 0.0001 | 11.0 | 78188 | 1.4835 | 0.8393 |
| 0.0013 | 12.0 | 85296 | 1.3846 | 0.8541 |
| 0.0001 | 13.0 | 92404 | 1.3622 | 0.8507 |
| 0.0909 | 14.0 | 99512 | 1.4672 | 0.8487 |
| 0.0001 | 15.0 | 106620 | 1.4243 | 0.8571 |
| 0.0 | 16.0 | 113728 | 1.5627 | 0.8582 |
| 0.0 | 17.0 | 120836 | 1.8146 | 0.8531 |
| 0.0 | 18.0 | 127944 | 1.8596 | 0.8550 |
| 0.0 | 19.0 | 135052 | 1.9233 | 0.8574 |
| 0.0 | 20.0 | 142160 | 1.9875 | 0.8569 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2