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ggallipoli/
t5-large_pos2neg

Name: t5-large_pos2neg
Availability: InStock

text-generation-inference

endpoints_compatible

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Text Style Transfer using CycleGANs

This repository contains the models from the paper "Self-supervised Text Style Transfer using Cycle-Consistent Adversarial Networks" (ACM TIST 2024).
The work introduces a novel approach to Text Style Transfer using CycleGANs with sequence-level supervision and Transformer architectures.

Available Models

Formality transfer

GYAFC dataset (Family & Relationships)

model	checkpoint
BART base	informal-to-formal, formal-to-informal
BART large	informal-to-formal, formal-to-informal
T5 small	informal-to-formal, formal-to-informal
T5 base	informal-to-formal, formal-to-informal
T5 large	informal-to-formal, formal-to-informal
BERT base	style classifier

GYAFC dataset (Entertainment & Music)

model	checkpoint
BART base	informal-to-formal, formal-to-informal
BART large	informal-to-formal, formal-to-informal
T5 small	informal-to-formal, formal-to-informal
T5 base	informal-to-formal, formal-to-informal
T5 large	informal-to-formal, formal-to-informal
BERT base	style classifier

Sentiment transfer

Yelp dataset

model	checkpoint
BART base	negative-to-positive, positive-to-negative
BART large	negative-to-positive, positive-to-negative
T5 small	negative-to-positive, positive-to-negative
T5 base	negative-to-positive, positive-to-negative
T5 large	negative-to-positive, positive-to-negative
BERT base	style classifier

Model Description

The models implement a CycleGAN architecture for Text Style Transfer that:

Applies self-supervision directly at sequence level
Maintains content while transferring style attributes
Employs pre-trained style classifiers to guide generation
Uses Transformer-based generators and discriminators

The models achieve state-of-the-art results on both formality and sentiment transfer tasks.

Usage

Both generators and style classifiers can be used with the Hugging Face 🤗 transformers library:

Each generator model can be loaded as:

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("[GENERATOR_MODEL]")
tokenizer = AutoTokenizer.from_pretrained("[GENERATOR_MODEL]")

The style classifiers can be loaded as:

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

classifier = AutoModelForSequenceClassification.from_pretrained("[CLASSIFIER_MODEL]")
tokenizer = AutoTokenizer.from_pretrained("[CLASSIFIER_MODEL]")

Citation

For more details, you can refer to the paper.

bibtex

@article{10.1145/3678179,
author = {La Quatra, Moreno and Gallipoli, Giuseppe and Cagliero, Luca},
title = {Self-supervised Text Style Transfer Using Cycle-Consistent Adversarial Networks},
year = {2024},
issue_date = {October 2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {15},
number = {5},
issn = {2157-6904},
url = {https://doi.org/10.1145/3678179},
doi = {10.1145/3678179},
journal = {ACM Trans. Intell. Syst. Technol.},
month = nov,
articleno = {110},
numpages = {38},
keywords = {Text Style Transfer, Sentiment transfer, Formality transfer, Cycle-consistent Generative Adversarial Networks, Transformers}
}

Code

The full implementation is available at: https://github.com/gallipoligiuseppe/TST-CycleGAN.

License

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

ggallipoli/t5-large_pos2neg