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-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

distilhubert-finetuned-babycry-v5

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: 0.8441
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