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-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: 0.8130081300813008
          - name: F1
            type: f1
            value: 0.7606844060819746
          - name: Precision
            type: precision
            value: 0.7167376435669118
          - name: Recall
            type: recall
            value: 0.8130081300813008

distilhubert-finetuned-donateacry

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: 0.7024
Accuracy: 0.8130
F1: 0.7607
Precision: 0.7167
Recall: 0.8130

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: 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	0.9032	7	1.0334	0.7317	0.6183	0.5354	0.7317
No log	1.9355	15	0.9193	0.7642	0.6866	0.6238	0.7642
No log	2.9677	23	0.7766	0.8049	0.7460	0.7005	0.8049
No log	4.0	31	0.8394	0.7724	0.7275	0.6889	0.7724
No log	4.9032	38	0.7391	0.7805	0.7351	0.6962	0.7805
No log	5.9355	46	0.7578	0.8130	0.7607	0.7167	0.8130
No log	6.9677	54	0.6822	0.8049	0.7558	0.7147	0.8049
No log	8.0	62	0.6980	0.8049	0.7543	0.7119	0.8049
No log	8.9032	69	0.7024	0.8130	0.7607	0.7167	0.8130

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1