metadata

library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - f1
  - precision
  - recall
model-index:
  - name: my_awesome_mind_model
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: audiofolder
          type: audiofolder
          config: initial_audio
          split: test
          args: initial_audio
        metrics:
          - name: F1
            type: f1
            value: 0.2564102564102564
          - name: Precision
            type: precision
            value: 0.7142857142857143
          - name: Recall
            type: recall
            value: 0.15625

my_awesome_mind_model

This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6889
F1: 0.2564
Precision: 0.7143
Recall: 0.1562

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall
No log	1.0	2	0.6914	0.2162	0.8	0.125
No log	2.0	4	0.6894	0.4815	0.5909	0.4062
No log	3.0	6	0.6887	0.3256	0.6364	0.2188
No log	4.0	8	0.6881	0.3415	0.7778	0.2188
0.6907	5.0	10	0.6883	0.3415	0.7778	0.2188
0.6907	6.0	12	0.6890	0.2564	0.7143	0.1562
0.6907	7.0	14	0.6894	0.2564	0.7143	0.1562
0.6907	8.0	16	0.6894	0.2105	0.6667	0.125
0.6907	9.0	18	0.6890	0.2564	0.7143	0.1562
0.6851	10.0	20	0.6889	0.2564	0.7143	0.1562

Framework versions

Transformers 4.44.2
Pytorch 2.4.1
Datasets 3.0.0
Tokenizers 0.19.1