|
--- |
|
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-donateacry |
|
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: 0.8369565217391305 |
|
- name: F1 |
|
type: f1 |
|
value: 0.7626704399279649 |
|
- name: Precision |
|
type: precision |
|
value: 0.7004962192816635 |
|
- name: Recall |
|
type: recall |
|
value: 0.8369565217391305 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# distilhubert-finetuned-donateacry |
|
|
|
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6654 |
|
- Accuracy: 0.8370 |
|
- F1: 0.7627 |
|
- Precision: 0.7005 |
|
- Recall: 0.8370 |
|
|
|
## 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: 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: 0.03 |
|
- num_epochs: 8 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| No log | 0.8696 | 5 | 0.6723 | 0.8370 | 0.7627 | 0.7005 | 0.8370 | |
|
| No log | 1.9130 | 11 | 0.6778 | 0.8370 | 0.7627 | 0.7005 | 0.8370 | |
|
| No log | 2.9565 | 17 | 0.6690 | 0.8370 | 0.7627 | 0.7005 | 0.8370 | |
|
| No log | 4.0 | 23 | 0.6654 | 0.8370 | 0.7627 | 0.7005 | 0.8370 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
