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: 0.9852941176470589
          - name: F1
            type: f1
            value: 0.9853150765112866
          - name: Precision
            type: precision
            value: 0.9853868369053048
          - name: Recall
            type: recall
            value: 0.9852941176470589

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: 0.0332
Accuracy: 0.9853
F1: 0.9853
Precision: 0.9854
Recall: 0.9853

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	0.9412	12	0.1931	0.9363	0.9365	0.9372	0.9363
No log	1.9608	25	0.0950	0.9706	0.9704	0.9710	0.9706
No log	2.9804	38	0.0611	0.9804	0.9804	0.9804	0.9804
No log	4.0	51	0.0492	0.9853	0.9853	0.9853	0.9853
No log	4.9412	63	0.0588	0.9804	0.9805	0.9814	0.9804
No log	5.9608	76	0.0368	0.9853	0.9853	0.9854	0.9853
No log	6.9804	89	0.0382	0.9902	0.9902	0.9903	0.9902
No log	8.0	102	0.0318	0.9951	0.9951	0.9951	0.9951
No log	8.9412	114	0.0331	0.9853	0.9853	0.9854	0.9853
No log	9.4118	120	0.0332	0.9853	0.9853	0.9854	0.9853

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1