metadata

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

distilhubert-finetuned-cry-detector

This model is a fine-tuned version of 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