---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: heartbeat-detection
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train[:90]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9629629629629629
---

<!-- 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. -->

# heartbeat-detection

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4766
- Accuracy: 0.9630

## 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: 1e-06
- 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: 0.01
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6241        | 1.0   | 8    | 1.6177          | 0.0      |
| 1.6057        | 2.0   | 16   | 1.5995          | 0.0      |
| 1.5872        | 3.0   | 24   | 1.5829          | 0.0370   |
| 1.5703        | 4.0   | 32   | 1.5674          | 0.0741   |
| 1.5557        | 5.0   | 40   | 1.5532          | 0.2593   |
| 1.5415        | 6.0   | 48   | 1.5401          | 0.4815   |
| 1.5285        | 7.0   | 56   | 1.5282          | 0.7037   |
| 1.5172        | 8.0   | 64   | 1.5175          | 0.7778   |
| 1.5074        | 9.0   | 72   | 1.5080          | 0.8519   |
| 1.4975        | 10.0  | 80   | 1.4998          | 0.8519   |
| 1.4906        | 11.0  | 88   | 1.4928          | 0.9259   |
| 1.4844        | 12.0  | 96   | 1.4870          | 0.9259   |
| 1.4788        | 13.0  | 104  | 1.4825          | 0.9630   |
| 1.4744        | 14.0  | 112  | 1.4793          | 0.9630   |
| 1.4718        | 15.0  | 120  | 1.4773          | 0.9630   |
| 1.4704        | 16.0  | 128  | 1.4766          | 0.9630   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2