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Positive Perspectives with English Text Reframing

Model description

This model is a T5-base 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:

Input:

['growth']: totally fed up with this bid now! :-( haven't even thought about my presentation yet :-(

Available sentiment strategies:

Strategy Description
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-en")

text = "['growth']: totally fed up with this bid now! :-( haven't even thought about my presentation yet :-("

pipe(text, max_length=1024)

Output:

# I haven't thought about my presentation yet, but I'm going to work hard to improve #my presentation, and I'll be better soon.
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