|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- marsyas/gtzan |
|
metrics: |
|
- accuracy |
|
base_model: ntu-spml/distilhubert |
|
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: 0.5358 |
|
- Accuracy: 0.88 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- 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 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 1.8758 | 1.0 | 57 | 1.7723 | 0.51 | |
|
| 1.2291 | 2.0 | 114 | 1.1713 | 0.69 | |
|
| 0.8029 | 3.0 | 171 | 0.8953 | 0.75 | |
|
| 0.7314 | 4.0 | 228 | 0.8242 | 0.73 | |
|
| 0.3424 | 5.0 | 285 | 0.6117 | 0.82 | |
|
| 0.229 | 6.0 | 342 | 0.5272 | 0.82 | |
|
| 0.1571 | 7.0 | 399 | 0.5470 | 0.87 | |
|
| 0.0777 | 8.0 | 456 | 0.5393 | 0.88 | |
|
| 0.0539 | 9.0 | 513 | 0.5087 | 0.88 | |
|
| 0.0688 | 10.0 | 570 | 0.5358 | 0.88 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.30.0.dev0 |
|
- Pytorch 2.0.1+cu117 |
|
- Datasets 2.13.1 |
|
- Tokenizers 0.13.3 |
|
|
