|
|
--- |
|
|
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: 1.2454 |
|
|
- Accuracy: 0.82 |
|
|
|
|
|
## 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: 4 |
|
|
- eval_batch_size: 4 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 2 |
|
|
- total_train_batch_size: 8 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_ratio: 0.2 |
|
|
- num_epochs: 40 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
|
| 2.2107 | 1.0 | 112 | 2.2411 | 0.31 | |
|
|
| 2.0193 | 2.0 | 225 | 1.9900 | 0.53 | |
|
|
| 1.7491 | 3.0 | 337 | 1.6436 | 0.59 | |
|
|
| 1.5096 | 4.0 | 450 | 1.3625 | 0.63 | |
|
|
| 0.9801 | 5.0 | 562 | 1.0769 | 0.75 | |
|
|
| 0.8603 | 6.0 | 675 | 0.9399 | 0.78 | |
|
|
| 0.5573 | 7.0 | 787 | 0.8290 | 0.77 | |
|
|
| 0.5776 | 8.0 | 900 | 0.6834 | 0.82 | |
|
|
| 0.4687 | 9.0 | 1012 | 0.6522 | 0.82 | |
|
|
| 0.3513 | 10.0 | 1125 | 0.6564 | 0.82 | |
|
|
| 0.1691 | 11.0 | 1237 | 0.6628 | 0.84 | |
|
|
| 0.0384 | 12.0 | 1350 | 0.8602 | 0.81 | |
|
|
| 0.0218 | 13.0 | 1462 | 0.8367 | 0.85 | |
|
|
| 0.0057 | 14.0 | 1575 | 0.9951 | 0.83 | |
|
|
| 0.0041 | 15.0 | 1687 | 1.0021 | 0.84 | |
|
|
| 0.0027 | 16.0 | 1800 | 1.0215 | 0.82 | |
|
|
| 0.0021 | 17.0 | 1912 | 0.9737 | 0.83 | |
|
|
| 0.0017 | 18.0 | 2025 | 1.0321 | 0.85 | |
|
|
| 0.0015 | 19.0 | 2137 | 0.9519 | 0.81 | |
|
|
| 0.0013 | 20.0 | 2250 | 0.9298 | 0.82 | |
|
|
| 0.0011 | 21.0 | 2362 | 0.9627 | 0.83 | |
|
|
| 0.001 | 22.0 | 2475 | 1.1373 | 0.82 | |
|
|
| 0.0009 | 23.0 | 2587 | 1.0855 | 0.83 | |
|
|
| 0.0008 | 24.0 | 2700 | 0.9979 | 0.81 | |
|
|
| 0.0008 | 25.0 | 2812 | 1.0956 | 0.82 | |
|
|
| 0.0009 | 26.0 | 2925 | 0.9861 | 0.82 | |
|
|
| 0.0007 | 27.0 | 3037 | 1.1387 | 0.83 | |
|
|
| 0.0006 | 28.0 | 3150 | 1.1965 | 0.83 | |
|
|
| 0.0006 | 29.0 | 3262 | 1.1527 | 0.81 | |
|
|
| 0.0007 | 30.0 | 3375 | 1.0609 | 0.82 | |
|
|
| 0.0006 | 31.0 | 3487 | 1.1770 | 0.81 | |
|
|
| 0.0801 | 32.0 | 3600 | 1.2290 | 0.82 | |
|
|
| 0.0005 | 33.0 | 3712 | 1.1785 | 0.83 | |
|
|
| 0.0005 | 34.0 | 3825 | 1.2154 | 0.83 | |
|
|
| 0.0004 | 35.0 | 3937 | 1.2250 | 0.83 | |
|
|
| 0.0004 | 36.0 | 4050 | 1.2280 | 0.82 | |
|
|
| 0.0004 | 37.0 | 4162 | 1.2364 | 0.83 | |
|
|
| 0.0004 | 38.0 | 4275 | 1.2379 | 0.82 | |
|
|
| 0.0004 | 39.0 | 4387 | 1.2483 | 0.83 | |
|
|
| 0.0004 | 39.82 | 4480 | 1.2454 | 0.82 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.29.2 |
|
|
- Pytorch 2.0.1 |
|
|
- Datasets 2.12.0 |
|
|
- Tokenizers 0.13.2 |
|
|
|
