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-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: 0.989010989010989
- name: F1
type: f1
value: 0.9890405015532383
- name: Precision
type: precision
value: 0.9891330367917903
- name: Recall
type: recall
value: 0.989010989010989
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: 0.0398
- Accuracy: 0.9890
- F1: 0.9890
- Precision: 0.9891
- Recall: 0.9890
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 0.9956 | 85 | 0.0769 | 0.9758 | 0.9760 | 0.9764 | 0.9758 |
| No log | 1.9912 | 170 | 0.0444 | 0.9875 | 0.9876 | 0.9876 | 0.9875 |
| No log | 2.9868 | 255 | 0.0398 | 0.9890 | 0.9890 | 0.9891 | 0.9890 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1