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---
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: 0.87
---
<!-- 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: 0.7345
- Accuracy: 0.87
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2637 | 1.0 | 75 | 2.2059 | 0.34 |
| 1.8944 | 2.0 | 150 | 1.8194 | 0.41 |
| 1.5462 | 3.0 | 225 | 1.4462 | 0.6 |
| 1.27 | 4.0 | 300 | 1.1931 | 0.66 |
| 1.0759 | 5.0 | 375 | 0.9130 | 0.76 |
| 0.6731 | 6.0 | 450 | 0.8307 | 0.75 |
| 0.5021 | 7.0 | 525 | 0.6785 | 0.82 |
| 0.351 | 8.0 | 600 | 0.6946 | 0.8 |
| 0.259 | 9.0 | 675 | 0.5913 | 0.82 |
| 0.1789 | 10.0 | 750 | 0.6499 | 0.83 |
| 0.0655 | 11.0 | 825 | 0.5624 | 0.88 |
| 0.1194 | 12.0 | 900 | 0.6549 | 0.83 |
| 0.0874 | 13.0 | 975 | 0.6412 | 0.86 |
| 0.0142 | 14.0 | 1050 | 0.7119 | 0.86 |
| 0.0119 | 15.0 | 1125 | 0.7415 | 0.85 |
| 0.0093 | 16.0 | 1200 | 0.6833 | 0.87 |
| 0.0089 | 17.0 | 1275 | 0.7802 | 0.85 |
| 0.0142 | 18.0 | 1350 | 0.7611 | 0.85 |
| 0.0072 | 19.0 | 1425 | 0.7262 | 0.86 |
| 0.057 | 20.0 | 1500 | 0.7345 | 0.87 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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