metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
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: 0.96045197740113
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: 0.0929
- Accuracy: 0.9605
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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 11 | 0.1843 | 0.9322 |
| No log | 2.0 | 22 | 0.1730 | 0.9379 |
| No log | 3.0 | 33 | 0.1143 | 0.9492 |
| No log | 4.0 | 44 | 0.0885 | 0.9661 |
| No log | 5.0 | 55 | 0.0929 | 0.9605 |
Framework versions
- Transformers 4.43.4
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1