metadata

tags:
  - generated_from_trainer
model-index:
  - name: asrtbsc_phi-freezed-best
    results:
      - task:
          type: dialogue act classification
        dataset:
          name: asapp/slue-phase-2
          type: hvb
        metrics:
          - name: F1 macro E2E
            type: F1 macro
            value: TBA
          - name: F1 macro GT
            type: F1 macro
            value: TBA
datasets:
  - asapp/slue-phase-2
language:
  - en
metrics:
  - f1-macro

asrtbsc_phi-freezed-best

ASR transcripts multi-label DAC

Model description

Backbone: Phi 3 mini
Pooling: Weighted mean Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween

Training and evaluation data

Trained on ASR transcripts.
Evaluated on ground truth (GT) and normalized Whisper small transcripts (E2E).

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1