This repository contains the checkpoint for the mT5 Base model fine-tuned on Formal-to-informal style transfer on the Italian subset of the XFORMAL dataset as part of the experiments of the paper IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation by Gabriele Sarti and Malvina Nissim.

A comprehensive overview of other released materials is provided in the gsarti/it5 repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.

Using the model

Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:

python

from transformers import pipelines

f2i = pipeline("text2text-generation", model='it5/mt5-base-formal-to-informal')
f2i("Vi ringrazio infinitamente per vostra disponibilità")
>>> [{"generated_text": "e grazie per la vostra disponibilità!"}]

or loaded using autoclasses:

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("it5/mt5-base-formal-to-informal")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-base-formal-to-informal")

If you use this model in your research, please cite our work as:

bibtex

@article{sarti-nissim-2022-it5,
    title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
    author={Sarti, Gabriele and Nissim, Malvina},
    journal={ArXiv preprint TBD},
    url={TBD},
    year={2022}
}

gsarti/mt5-base-formal-to-informal

mT5 Base for Formal-to-informal Style Transfer 🤗

Using the model

gsarti/
mt5-base-formal-to-informal