readme: ---
  language: pt
  tags:
    - seq2seq
    - t5
    - positive_perspectives
  datasets:
    - dominguesm/positive-reframing-ptbr-dataset
  widget:
    - text: "['growth', 'neutralizing']: Sempre estressado e pensando em um monte de coisas ao mesmo tempo, preciso levar uma de cada vez, sobrecarga estressada, necessidade de reclamar"
    - text: "['growth', 'neutralizing', 'optimism']: Se eu não tiver um colapso mental antes do final do verão, será um milagre."
    - text: "['impermanence']: Dirigindo para visitar a vovó no hospital e o meu filho que está doente."
    - text: "['optimism']: Ótimo agora, como vou explicar isso para ela, ela está tão perto de mim que não posso perdê-la :'("
    - text: "['growth', 'optimism']: sempre há algo que eu poderia estar fazendo. Eu geralmente escolho não fazer isso."
  ---
  
  # Positive Perspectives with Portuguese Text Reframing
  
  ## Model description
  
   This model is a [PTT5](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. Based on the article [arXiv:2204.02952](https://arxiv.org/abs/2204.02952). 
   
  ## How to use
  
  The model uses one or more sentiment strategies concatenated with a sentence and will generate a sentence with the applied sentiment output. The maximum string length is 1024 tokens. Entries must be organized in the following format:
  
  ```
  "['thankfulness', 'optimism']: Tenho tanta coisa para fazer antes de sair da cidade por uma semana no domingo."
  ```
  
  ### Available sentiment strategies:
  
  **growth**: viewing a challenging event as an opportunity for the author to specifically grow or improve himself.
  
  **impermanence**: Saying that bad things don't last forever, will get better soon, and/or that other people have had similar difficulties.
  
  **neutralizing**: Replacing a negative word with a neutral word. For example, “This was a terrible day” becomes “This was a long day”.
  
  **optimism**: Focusing on things about the situation itself, at that moment, that are good (not just predicting a better future).
  
  **self_affirmation**: Talking about what strengths the author already has, or values he admires, such as love, courage, perseverance, etc.
  
  **thankfulness**: Expressing gratitude or gratitude with keywords like appreciate, happy for it, grateful for, good thing, etc.
  
  ### Usage
  
  ```python
  from transformers import pipeline
  
  pipe = pipeline('summarization', "dominguesm/positive-reframing-ptbr")
  
  text = "['thankfulness', 'optimism']: Tenho tanta coisa para fazer antes de sair da cidade por uma semana no domingo."
  
  pipe(text, max_length=1024)
  
  ```