Positive Perspectives with English Text Reframing
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:
['growth']: totally fed up with this bid now! :-( haven't even thought about my presentation yet :-(
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.|
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)
# 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.
- Downloads last month
This model can be loaded on the Inference API on-demand.