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-v6
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: 0.8260869565217391
- name: F1
type: f1
value: 0.7474120082815735
- name: Precision
type: precision
value: 0.6824196597353497
- name: Recall
type: recall
value: 0.8260869565217391
distilhubert-finetuned-babycry-v6
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: 0.6676
- Accuracy: {'accuracy': 0.8260869565217391}
- F1: 0.7474
- Precision: 0.6824
- Recall: 0.8261
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: 4
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|---|---|---|
| 0.7816 | 2.1739 | 25 | 0.7361 | {'accuracy': 0.8260869565217391} | 0.7474 | 0.6824 | 0.8261 |
| 0.7056 | 4.3478 | 50 | 0.6957 | {'accuracy': 0.8260869565217391} | 0.7474 | 0.6824 | 0.8261 |
| 0.6654 | 6.5217 | 75 | 0.6683 | {'accuracy': 0.8260869565217391} | 0.7474 | 0.6824 | 0.8261 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1