---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilhubert-finetuned-babycry-v5
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.782608695652174
    - name: F1
      type: f1
      value: 0.6871686108165429
    - name: Precision
      type: precision
      value: 0.6124763705103969
    - name: Recall
      type: recall
      value: 0.782608695652174
---

<!-- 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-babycry-v5

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: 0.8441
- Accuracy: {'accuracy': 0.782608695652174}
- F1: 0.6872
- Precision: 0.6125
- Recall: 0.7826

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy                        | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:-------------------------------:|:------:|:---------:|:------:|
| 0.7047        | 1.0870 | 25   | 0.9225          | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125    | 0.7826 |
| 0.6071        | 2.1739 | 50   | 0.9175          | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125    | 0.7826 |
| 0.6525        | 3.2609 | 75   | 0.8866          | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125    | 0.7826 |
| 0.6558        | 4.3478 | 100  | 0.8433          | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125    | 0.7826 |
| 0.5577        | 5.4348 | 125  | 0.8705          | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125    | 0.7826 |
| 0.7055        | 6.5217 | 150  | 0.8323          | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125    | 0.7826 |
| 0.6092        | 7.6087 | 175  | 0.8440          | {'accuracy': 0.782608695652174} | 0.6872 | 0.6125    | 0.7826 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1