---
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