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metadata
license: apache-2.0
base_model: facebook/wav2vec2-conformer-rel-pos-large
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: wav2vec2-conformer-rel-pos-large-medical-intent-v2
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6169590643274854
          - name: Precision
            type: precision
            value: 0.6350528050296339
          - name: Recall
            type: recall
            value: 0.6169590643274854

wav2vec2-conformer-rel-pos-large-medical-intent-v2

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

  • Loss: 1.0410
  • Accuracy: 0.6170
  • Precision: 0.6351
  • Recall: 0.6170

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall
1.7714 1.0 82 1.7605 0.2339 0.3198 0.2339
1.511 2.0 164 1.5148 0.4298 0.3817 0.4298
1.1417 2.99 246 1.1530 0.5936 0.6491 0.5936
0.8747 3.99 328 1.0410 0.6170 0.6351 0.6170

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2