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---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
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: 0.8932584269662921
---
<!-- 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: 0.5034
- Accuracy: 0.8933
## 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: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9888 | 11 | 0.9525 | 0.7303 |
| No log | 1.9775 | 22 | 1.2765 | 0.5393 |
| No log | 2.9663 | 33 | 0.6634 | 0.7978 |
| No log | 3.9551 | 44 | 0.6369 | 0.8202 |
| No log | 4.9438 | 55 | 0.5328 | 0.8596 |
| No log | 5.9326 | 66 | 0.5146 | 0.8652 |
| No log | 6.9213 | 77 | 0.5200 | 0.8764 |
| No log | 8.0 | 89 | 0.5213 | 0.8708 |
| No log | 8.9888 | 100 | 0.6062 | 0.8596 |
| No log | 9.9775 | 111 | 0.5938 | 0.8652 |
| No log | 10.9663 | 122 | 0.5247 | 0.8652 |
| No log | 11.9551 | 133 | 0.7004 | 0.8483 |
| No log | 12.9438 | 144 | 0.5388 | 0.8876 |
| No log | 13.9326 | 155 | 0.4856 | 0.8876 |
| No log | 14.9213 | 166 | 0.5380 | 0.8764 |
| No log | 16.0 | 178 | 0.5055 | 0.8876 |
| No log | 16.9888 | 189 | 0.5217 | 0.8876 |
| No log | 17.9775 | 200 | 0.5034 | 0.8933 |
| No log | 18.9663 | 211 | 0.4745 | 0.8876 |
| No log | 19.9551 | 222 | 0.4812 | 0.8876 |
| No log | 20.9438 | 233 | 0.4709 | 0.8820 |
| No log | 21.9326 | 244 | 0.4824 | 0.8876 |
| No log | 22.9213 | 255 | 0.4819 | 0.8876 |
| No log | 24.0 | 267 | 0.4877 | 0.8933 |
| No log | 24.7191 | 275 | 0.4866 | 0.8933 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|