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: 0.8876404494382022
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: 0.4928
- Accuracy: 0.8876
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9888 | 11 | 1.3978 | 0.5899 |
| No log | 1.9775 | 22 | 1.0485 | 0.7022 |
| No log | 2.9663 | 33 | 0.8464 | 0.8258 |
| No log | 3.9551 | 44 | 0.6916 | 0.8596 |
| No log | 4.9438 | 55 | 0.6074 | 0.8596 |
| No log | 5.9326 | 66 | 0.5557 | 0.8708 |
| No log | 6.9213 | 77 | 0.5206 | 0.8764 |
| No log | 8.0 | 89 | 0.4942 | 0.8876 |
| No log | 8.9888 | 100 | 0.4945 | 0.8876 |
| No log | 9.8876 | 110 | 0.4928 | 0.8876 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1