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---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
- precision
- recall
- f1
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
- name: Precision
type: precision
value: 0.8802816627816629
- name: Recall
type: recall
value: 0.87
- name: F1
type: f1
value: 0.8627110595989314
---
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/8epo656a)
# 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: 0.6501
- Accuracy: 0.87
- Precision: 0.8803
- Recall: 0.87
- F1: 0.8627
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.1743 | 1.0 | 113 | 2.0604 | 0.38 | 0.5273 | 0.38 | 0.3101 |
| 1.6179 | 2.0 | 226 | 1.4299 | 0.62 | 0.6136 | 0.62 | 0.5877 |
| 1.0981 | 3.0 | 339 | 1.0223 | 0.79 | 0.8516 | 0.79 | 0.7669 |
| 0.9785 | 4.0 | 452 | 0.8722 | 0.71 | 0.7748 | 0.71 | 0.6733 |
| 0.8834 | 5.0 | 565 | 0.8363 | 0.76 | 0.7691 | 0.76 | 0.7449 |
| 0.4936 | 6.0 | 678 | 0.6241 | 0.82 | 0.8313 | 0.82 | 0.8193 |
| 0.2772 | 7.0 | 791 | 0.5648 | 0.85 | 0.8623 | 0.85 | 0.8459 |
| 0.1213 | 8.0 | 904 | 0.6919 | 0.81 | 0.8429 | 0.81 | 0.7997 |
| 0.0958 | 9.0 | 1017 | 0.5527 | 0.86 | 0.8682 | 0.86 | 0.8541 |
| 0.0194 | 10.0 | 1130 | 0.6840 | 0.85 | 0.8645 | 0.85 | 0.8420 |
| 0.0151 | 11.0 | 1243 | 0.6214 | 0.86 | 0.8642 | 0.86 | 0.8542 |
| 0.1239 | 12.0 | 1356 | 0.6501 | 0.87 | 0.8803 | 0.87 | 0.8627 |
| 0.0049 | 13.0 | 1469 | 0.6651 | 0.87 | 0.8803 | 0.87 | 0.8627 |
| 0.0043 | 14.0 | 1582 | 0.7188 | 0.87 | 0.8803 | 0.87 | 0.8627 |
| 0.0035 | 15.0 | 1695 | 0.6808 | 0.87 | 0.8803 | 0.87 | 0.8627 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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