readme: ---
  license: mit
  ---
  
  Base model: [roberta-large](https://huggingface.co/roberta-large)
  
  Fine tuned for persuadee donation detection on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019):
  
  Given a complete dialogue from Persuasion For Good, the task is to predict the binary label:
   - 0: the persuadee does not intend to donate
   - 1: the persuadee intends to donate
  
  Only persuadee utterances are input to the model for this task - persuader utterances are discarded. Each training example is the concatenation of all persuadee utterances in a single dialogue, each separated by the `</s>` token.
  
  For example:
  
  **Input**: `<s>How are you?</s>Can you tell me more about the charity?</s>...</s>Sure, I'll donate a dollar.</s>...</s>`
  
  **Label**: 1
  
  **Input**: `<s>How are you?</s>Can you tell me more about the charity?</s>...</s>I am not interested.</s>...</s>`
  
  **Label**: 0
  
  The following Dialogues were excluded:
   - 146 dialogues where a donation of 0 was made at the end of the task but a non-zero amount was pledged by the persuadee in the dialogue, per the following regular expression: `(?:\$(?:0\.)?[1-9]|[1-9][.0-9]*?(?: ?\$| dollars?| cents?))`
   
  Data Info:
   - **Training set**: 587 dialogues, using actual end-task donations as labels
   - **Validation set**: 141 dialogues, using manual donation intention labels from Persuasion For Good 'AnnSet'
   - **Test set**: 143 dialogues, using manual donation intention labels from Persuasion For Good 'AnnSet'
  
  Training Info:
   - **Loss**: CrossEntropy with class weights: 1.5447 (class 0) and 0.7393 (class 1). These weights were derived from the training split.
   - **Early Stopping**: The checkpoint with the highest validation macro f1 was selected. This occurred at step 35 (see training metrics for more detail).
  
  Testing Info:
   - **Test Macro F1**: 0.893
   - **Test Accuracy**: 0.902