transformers pytorch safetensors t5 text2text-generation seq2seq positive_perspectives en arxiv:2204.02952 license:cc-by-4.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language: en
license: cc-by-4.0
tags:
- seq2seq
- t5
- positive_perspectives
widget:
- text: '[''neutralizing'', ''optimism'']: Ugh I have to wake up so early (9:00) and
    go to class (a massage). I have so much (so little) to do today.'
- text: '[''growth'']: You know I really don''t care about the power struggle between
    the papacy and secular authority in the medieval ages. stupid'
- text: '[''neutralizing'', ''optimism'']: thinking about my future makes me want
    to go live on a island alone forever. annoyed'
- text: '[''neutralizing'', ''optimism'']: Honestly don''t know how I''m going to
    finish all of this homework and projects! homework FAIL Tired FML'
- text: '[''neutralizing'', ''optimism'', ''thankfulness'']: Who would have ever guessed
    that it would be so freaking hard to get three different grades from two different
    schools together.'
---

# Positive Perspectives with English Text Reframing

## Model description

 This model is a [T5-base](https://huggingface.co/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](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:

**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

```python
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.
```