|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- marsyas/gtzan |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: distilhubert-finetuned-gtzan |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# distilhubert-finetuned-gtzan |
|
|
|
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.0908 |
|
- Accuracy: 0.84 |
|
|
|
## 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: 30 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 2.1531 | 1.0 | 113 | 2.1667 | 0.41 | |
|
| 1.6622 | 2.0 | 226 | 1.6138 | 0.63 | |
|
| 1.3112 | 3.0 | 339 | 1.2047 | 0.7 | |
|
| 0.9374 | 4.0 | 452 | 0.9595 | 0.73 | |
|
| 0.5475 | 5.0 | 565 | 0.7239 | 0.82 | |
|
| 0.4845 | 6.0 | 678 | 0.7406 | 0.75 | |
|
| 0.2489 | 7.0 | 791 | 0.6838 | 0.78 | |
|
| 0.3272 | 8.0 | 904 | 0.8447 | 0.79 | |
|
| 0.2244 | 9.0 | 1017 | 0.7184 | 0.81 | |
|
| 0.0353 | 10.0 | 1130 | 0.8800 | 0.79 | |
|
| 0.0201 | 11.0 | 1243 | 0.8800 | 0.83 | |
|
| 0.0079 | 12.0 | 1356 | 0.8207 | 0.83 | |
|
| 0.004 | 13.0 | 1469 | 0.9218 | 0.82 | |
|
| 0.003 | 14.0 | 1582 | 1.0004 | 0.83 | |
|
| 0.0024 | 15.0 | 1695 | 0.9446 | 0.84 | |
|
| 0.0021 | 16.0 | 1808 | 0.9802 | 0.85 | |
|
| 0.0018 | 17.0 | 1921 | 0.9766 | 0.84 | |
|
| 0.0017 | 18.0 | 2034 | 1.0597 | 0.84 | |
|
| 0.0014 | 19.0 | 2147 | 0.9541 | 0.84 | |
|
| 0.0012 | 20.0 | 2260 | 1.0408 | 0.84 | |
|
| 0.0011 | 21.0 | 2373 | 1.0364 | 0.84 | |
|
| 0.001 | 22.0 | 2486 | 1.0993 | 0.84 | |
|
| 0.001 | 23.0 | 2599 | 1.0620 | 0.84 | |
|
| 0.0009 | 24.0 | 2712 | 1.0193 | 0.83 | |
|
| 0.0009 | 25.0 | 2825 | 1.0164 | 0.83 | |
|
| 0.0009 | 26.0 | 2938 | 1.0293 | 0.84 | |
|
| 0.0008 | 27.0 | 3051 | 1.0478 | 0.84 | |
|
| 0.0008 | 28.0 | 3164 | 1.0727 | 0.84 | |
|
| 0.0008 | 29.0 | 3277 | 1.0773 | 0.84 | |
|
| 0.0008 | 30.0 | 3390 | 1.0908 | 0.84 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.30.0.dev0 |
|
- Pytorch 2.0.1+cu117 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
|
|
