---
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-cry-detector
  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: 0.991941391941392
    - name: F1
      type: f1
      value: 0.9919569277165429
    - name: Precision
      type: precision
      value: 0.9920048531706146
    - name: Recall
      type: recall
      value: 0.991941391941392
---

<!-- 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-cry-detector

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: 0.0408
- Accuracy: 0.9919
- F1: 0.9920
- Precision: 0.9920
- Recall: 0.9919

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 123
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.001
- num_epochs: 7

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.9956 | 85   | 0.0736          | 0.9788   | 0.9788 | 0.9790    | 0.9788 |
| No log        | 1.9912 | 170  | 0.0680          | 0.9758   | 0.9760 | 0.9770    | 0.9758 |
| No log        | 2.9985 | 256  | 0.0447          | 0.9875   | 0.9876 | 0.9876    | 0.9875 |
| No log        | 3.9941 | 341  | 0.0452          | 0.9905   | 0.9905 | 0.9905    | 0.9905 |
| No log        | 4.9898 | 426  | 0.0439          | 0.9919   | 0.9920 | 0.9920    | 0.9919 |
| 0.053         | 5.9971 | 512  | 0.0401          | 0.9919   | 0.9920 | 0.9920    | 0.9919 |
| 0.053         | 6.9693 | 595  | 0.0408          | 0.9919   | 0.9920 | 0.9920    | 0.9919 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1