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---
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:
- name: F1 macro E2E
type: F1 macro
value: 66.18
- name: F1 macro GT
type: F1 macro
value: 68.52
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
should probably proofread and complete it, then remove this comment. -->
# 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
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