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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-gtzan4
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: 0.78
---
<!-- 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-gtzan4
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: 1.0945
- Accuracy: 0.78
## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 192
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.85 | 4 | 2.2991 | 0.06 |
| 2.2997 | 1.92 | 9 | 2.2668 | 0.28 |
| 2.2819 | 2.99 | 14 | 2.1877 | 0.33 |
| 2.2336 | 3.84 | 18 | 2.1023 | 0.47 |
| 2.1493 | 4.91 | 23 | 1.9895 | 0.52 |
| 2.0571 | 5.97 | 28 | 1.8745 | 0.51 |
| 1.9341 | 6.83 | 32 | 1.7838 | 0.57 |
| 1.8274 | 7.89 | 37 | 1.6784 | 0.64 |
| 1.724 | 8.96 | 42 | 1.5859 | 0.66 |
| 1.6407 | 9.81 | 46 | 1.5234 | 0.66 |
| 1.5593 | 10.88 | 51 | 1.4508 | 0.7 |
| 1.4735 | 11.95 | 56 | 1.3982 | 0.69 |
| 1.4185 | 12.8 | 60 | 1.3501 | 0.72 |
| 1.3613 | 13.87 | 65 | 1.3131 | 0.74 |
| 1.3099 | 14.93 | 70 | 1.2742 | 0.72 |
| 1.2762 | 16.0 | 75 | 1.2485 | 0.73 |
| 1.2762 | 16.85 | 79 | 1.2102 | 0.74 |
| 1.2379 | 17.92 | 84 | 1.1931 | 0.75 |
| 1.193 | 18.99 | 89 | 1.1647 | 0.75 |
| 1.1863 | 19.84 | 93 | 1.1488 | 0.77 |
| 1.1435 | 20.91 | 98 | 1.1349 | 0.78 |
| 1.1424 | 21.97 | 103 | 1.1166 | 0.79 |
| 1.0961 | 22.83 | 107 | 1.1025 | 0.78 |
| 1.0887 | 23.89 | 112 | 1.0993 | 0.78 |
| 1.0977 | 24.96 | 117 | 1.0952 | 0.78 |
| 1.0661 | 25.6 | 120 | 1.0945 | 0.78 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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