gsarti commited on
Commit
58d5111
1 Parent(s): 5b4b3e3

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +18 -8
README.md CHANGED
@@ -15,7 +15,7 @@ thumbnail: https://gsarti.com/publication/it5/featured.png
15
 
16
  The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer).
17
 
18
- This model is released as part of the project ["IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://arxiv.org/abs/2203.03759), by [Gabriele Sarti](https://gsarti.com/) and [Malvina Nissim](https://malvinanissim.github.io/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
19
 
20
  *The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the [`it5`](https://huggingface.co/it5) organization provide some examples of this model fine-tuned on various downstream task.*
21
 
@@ -77,12 +77,22 @@ For problems or updates on this model, please contact [[email protected]
77
  ## Citation Information
78
 
79
  ```bibtex
80
- @article{sarti-nissim-2022-it5,
81
- title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
82
- author={Sarti, Gabriele and Nissim, Malvina},
83
- journal={ArXiv preprint 2203.03759},
84
- url={https://arxiv.org/abs/2203.03759},
85
- year={2022},
86
- month={mar}
 
 
 
 
 
 
 
 
 
 
87
  }
88
  ```
 
15
 
16
  The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer).
17
 
18
+ This model is released as part of the project ["IT5: Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://aclanthology.org/2024.lrec-main.823/), by [Gabriele Sarti](https://gsarti.com/) and [Malvina Nissim](https://malvinanissim.github.io/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
19
 
20
  *The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the [`it5`](https://huggingface.co/it5) organization provide some examples of this model fine-tuned on various downstream task.*
21
 
 
77
  ## Citation Information
78
 
79
  ```bibtex
80
+ @inproceedings{sarti-nissim-2024-it5-text,
81
+ title = "{IT}5: Text-to-text Pretraining for {I}talian Language Understanding and Generation",
82
+ author = "Sarti, Gabriele and
83
+ Nissim, Malvina",
84
+ editor = "Calzolari, Nicoletta and
85
+ Kan, Min-Yen and
86
+ Hoste, Veronique and
87
+ Lenci, Alessandro and
88
+ Sakti, Sakriani and
89
+ Xue, Nianwen",
90
+ booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
91
+ month = may,
92
+ year = "2024",
93
+ address = "Torino, Italia",
94
+ publisher = "ELRA and ICCL",
95
+ url = "https://aclanthology.org/2024.lrec-main.823",
96
+ pages = "9422--9433",
97
  }
98
  ```