transformers tensorboard safetensors cross-attention-sentence-classifier generated_from_trainer en dataset:asapp/slue-phase-2 model-index endpoints_compatible region:us
---
datasets:
- asapp/slue-phase-2
language:
- en
metrics:
- f1-macro
tags:
- generated_from_trainer
model-index:
- name: prosody_asrtbsc_distilbert-uncased-best
  results:
  - task:
      type: dialogue act classification
    dataset:
      name: asapp/slue-phase-2
      type: hvb
    metrics:
    - type: F1 macro
      value: 66.18
      name: F1 macro E2E
    - type: F1 macro
      value: 68.52
      name: F1 macro GT
---

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# prosody_asrtbsc_distilbert-uncased-best

Normalized ASR translations with prosody encoding cross attention multi-label DAC

## Model description

Prosody encoder: 2 layer transformer encoder with initial dense projection
Backbone: [DistilBert uncased](https://huggingface.co/distilbert/distilbert-base-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 normalized [Whisper small](https://huggingface.co/openai/whisper-small) transcripts.  
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: 0.001
- 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 2.19.1
- Tokenizers 0.19.1
