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---
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-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: 0.991941391941392
- name: F1
type: f1
value: 0.9919569277165429
- name: Precision
type: precision
value: 0.9920048531706146
- name: Recall
type: recall
value: 0.991941391941392
---
<!-- 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-cry-detector
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: 0.0408
- Accuracy: 0.9919
- F1: 0.9920
- Precision: 0.9920
- Recall: 0.9919
## 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
- lr_scheduler_warmup_ratio: 0.001
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 0.9956 | 85 | 0.0736 | 0.9788 | 0.9788 | 0.9790 | 0.9788 |
| No log | 1.9912 | 170 | 0.0680 | 0.9758 | 0.9760 | 0.9770 | 0.9758 |
| No log | 2.9985 | 256 | 0.0447 | 0.9875 | 0.9876 | 0.9876 | 0.9875 |
| No log | 3.9941 | 341 | 0.0452 | 0.9905 | 0.9905 | 0.9905 | 0.9905 |
| No log | 4.9898 | 426 | 0.0439 | 0.9919 | 0.9920 | 0.9920 | 0.9919 |
| 0.053 | 5.9971 | 512 | 0.0401 | 0.9919 | 0.9920 | 0.9920 | 0.9919 |
| 0.053 | 6.9693 | 595 | 0.0408 | 0.9919 | 0.9920 | 0.9920 | 0.9919 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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