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: 0.79
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: 1.0416
- Accuracy: 0.79
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: 16
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1363 | 1.0 | 113 | 2.0422 | 0.35 |
| 1.4702 | 2.0 | 226 | 1.3950 | 0.58 |
| 1.0964 | 3.0 | 339 | 1.0145 | 0.69 |
| 1.0044 | 4.0 | 452 | 0.9337 | 0.7 |
| 0.5048 | 5.0 | 565 | 0.8660 | 0.74 |
| 0.404 | 6.0 | 678 | 0.7419 | 0.78 |
| 0.3646 | 7.0 | 791 | 0.7474 | 0.76 |
| 0.115 | 8.0 | 904 | 0.7273 | 0.8 |
| 0.1827 | 9.0 | 1017 | 0.8134 | 0.8 |
| 0.0188 | 10.0 | 1130 | 0.8383 | 0.83 |
| 0.0129 | 11.0 | 1243 | 0.9425 | 0.8 |
| 0.0342 | 12.0 | 1356 | 0.9965 | 0.8 |
| 0.0075 | 13.0 | 1469 | 0.9938 | 0.8 |
| 0.0069 | 14.0 | 1582 | 1.0191 | 0.8 |
| 0.0063 | 15.0 | 1695 | 1.0215 | 0.79 |
| 0.0056 | 16.0 | 1808 | 1.0416 | 0.79 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3