README.md

metadata

tags:
  - generated_from_trainer
model-index:
  - name: prosody_gttbsc_distilbert-uncased-best
    results:
      - task:
          type: dialogue act classification
        dataset:
          name: asapp/slue-phase-2
          type: hvb
        metrics:
          - name: F1 macro E2E
            type: F1 macro
            value: 66.43
          - name: F1 macro GT
            type: F1 macro
            value: 72.7
datasets:
  - asapp/slue-phase-2
language:
  - en
metrics:
  - f1-macro

prosody_gttbsc_distilbert-uncased-best

Ground truth text with prosody encoding cross attention multi-label DAC

Model description

Prosody encoder: 2 layer transformer encoder with initial dense projection Backbone: DistilBert uncased
Pooling: Self attention
Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween

Training and evaluation data

Trained on ground truth slue-phase-2 hvb.
Evaluated on ground truth and normalized Whisper small transcripts.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0004
• 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
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1