README.md

---
tags:
- generated_from_trainer
model-index:
- name: fusion_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: 71.72
        - name: F1 macro GT
          type: F1 macro
          value: 73.48
datasets:
- asapp/slue-phase-2
language:
- en
metrics:
- f1-macro
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# fusion_gttbsc_distilbert-uncased-best

Ground truth text with prosody encoding and ASR encoding residual cross attention fusion multi-label DAC

## Model description

ASR encoder: [Whisper small](https://huggingface.co/openai/whisper-small) encoder  
Prosody encoder: 2 layer transformer encoder with initial dense projection  
Backbone: [DistilBert uncased](https://huggingface.co/distilbert/distilbert-base-uncased)  
Fusion: 2 residual cross attention fusion layers (F_asr x F_text and F_prosody x F_text) with dense layer on top  
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.  
Evaluated on ground truth (GT) and normalized [Whisper small](https://huggingface.co/openai/whisper-small) transcripts (E2E).  

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0007
- 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