language: pt
license: cc-by-4.0
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 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.
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
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)