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README.md

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+---
+language:
+- en
+---
+# 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](https://huggingface.co/ggallipoli/bart-base_inf2for_family), [formal-to-informal](https://huggingface.co/ggallipoli/bart-base_for2inf_family) |
+| BART large | [informal-to-formal](https://huggingface.co/ggallipoli/bart-large_inf2for_family), [formal-to-informal](https://huggingface.co/ggallipoli/bart-large_for2inf_family) |
+|  T5 small  | [informal-to-formal](https://huggingface.co/ggallipoli/t5-small_inf2for_family), [formal-to-informal](https://huggingface.co/ggallipoli/t5-small_for2inf_family) |
+|   T5 base  | [informal-to-formal](https://huggingface.co/ggallipoli/t5-base_inf2for_family), [formal-to-informal](https://huggingface.co/ggallipoli/t5-base_for2inf_family) |
+|  T5 large  | [informal-to-formal](https://huggingface.co/ggallipoli/t5-large_inf2for_family), [formal-to-informal](https://huggingface.co/ggallipoli/t5-large_for2inf_family) |
+|  BERT base |                [style classifier](https://huggingface.co/ggallipoli/formality_classifier_gyafc_family)                |
+
+#### GYAFC dataset (Entertainment & Music)
+
+|    model   |                       checkpoint                       |
+|:----------:|:------------------------------------------------------:|
+|  BART base | [informal-to-formal](https://huggingface.co/ggallipoli/bart-base_inf2for_music), [formal-to-informal](https://huggingface.co/ggallipoli/bart-base_for2inf_music) |
+| BART large | [informal-to-formal](https://huggingface.co/ggallipoli/bart-large_inf2for_music), [formal-to-informal](https://huggingface.co/ggallipoli/bart-large_for2inf_music) |
+|  T5 small  | [informal-to-formal](https://huggingface.co/ggallipoli/t5-small_inf2for_music), [formal-to-informal](https://huggingface.co/ggallipoli/t5-small_for2inf_music) |
+|   T5 base  | [informal-to-formal](https://huggingface.co/ggallipoli/t5-base_inf2for_music), [formal-to-informal](https://huggingface.co/ggallipoli/t5-base_for2inf_music) |
+|  T5 large  | [informal-to-formal](https://huggingface.co/ggallipoli/t5-large_inf2for_music), [formal-to-informal](https://huggingface.co/ggallipoli/t5-large_for2inf_music) |
+|  BERT base |                [style classifier](https://huggingface.co/ggallipoli/formality_classifier_gyafc_music)                |
+
+### Sentiment transfer
+#### Yelp dataset
+
+|    model   |                       checkpoint                       |
+|:----------:|:------------------------------------------------------:|
+|  BART base | [negative-to-positive](https://huggingface.co/ggallipoli/bart-base_neg2pos), [positive-to-negative](https://huggingface.co/ggallipoli/bart-base_pos2neg) |
+| BART large | [negative-to-positive](https://huggingface.co/ggallipoli/bart-large_neg2pos), [positive-to-negative](https://huggingface.co/ggallipoli/bart-large_pos2neg) |
+|  T5 small  | [negative-to-positive](https://huggingface.co/ggallipoli/t5-small_neg2pos), [positive-to-negative](https://huggingface.co/ggallipoli/t5-small_pos2neg) |
+|   T5 base  | [negative-to-positive](https://huggingface.co/ggallipoli/t5-base_neg2pos), [positive-to-negative](https://huggingface.co/ggallipoli/t5-base_pos2neg) |
+|  T5 large  | [negative-to-positive](https://huggingface.co/ggallipoli/t5-large_neg2pos), [positive-to-negative](https://huggingface.co/ggallipoli/t5-large_pos2neg) |
+|  BERT base |                [style classifier](https://huggingface.co/ggallipoli/sentiment_classifier_yelp)                |
+
+## 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](https://dl.acm.org/doi/10.1145/3678179).
+
+```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 <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.