It5 Base

🚀 Italian T5 Base 🇮🇹

The IT5 model family is the first attempt at pretraining large - scale sequence - to - sequence transformer models for the Italian language, following the approach of the original T5 model.

This model is part of the project "IT5: Text - to - Text Pretraining for Italian Language Understanding and Generation", by Gabriele Sarti and Malvina Nissim, supported by Huggingface, with TPU usage sponsored by Google's TPU Research Cloud. All training was done on a single TPU3v8 - VM machine on Google Cloud. Check the Tensorboard tab of the repository for an overview of the training process.

⚠️ Important Note

The inference widget is deactivated because the model needs a task - specific seq2seq fine - tuning on a downstream task to be useful in practice.

✨ Features

Model variants

This repository holds the checkpoints for the base version of the model. The model was trained for one epoch (1.05M steps) on the Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB) using 🤗 Datasets and the google/t5 - v1_1 - base improved configuration. Another version of this model trained on the [OSCAR corpus](https://oscar - corpus.com/) is available as [gsarti/it5 - base - oscar](https://huggingface.co/gsartiit5 - base - oscar). The training procedure is available [on Github](https://github.com/gsarti/t5 - flax - gcp).

The following table summarizes the parameters for all available models:

The high training time of it5 - base - oscar was due to a bug in the training script.

For a list of individual model parameters, refer to the config.json file in the respective repositories.

📦 Installation

No specific installation steps provided in the original README.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-base")

💡 Usage Tip

You will need to fine - tune the model on your downstream seq2seq task to use it. See an example here.

Advanced Usage

from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration

model_flax = FlaxT5ForConditionalGeneration.from_pretrained("gsarti/it5-base")
model_tf = TFT5ForConditionalGeneration.from_pretrained("gsarti/it5-base")

🔧 Technical Details

Due to the nature of the web - scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre - existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research - oriented and non - user - facing endeavors.

📄 License

This model is released under the apache - 2.0 license.

📚 Documentation

Model curators

For problems or updates on this model, please contact gabriele.sarti996@gmail.com.

Citation Information

@inproceedings{sarti-nissim-2024-it5-text,
    title = "{IT}5: Text-to-text Pretraining for {I}talian Language Understanding and Generation",
    author = "Sarti, Gabriele  and
      Nissim, Malvina",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.823",
    pages = "9422--9433",
    abstract = "We introduce IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian. We document and perform a thorough cleaning procedure for a large Italian corpus and use it to pretrain four IT5 model sizes. We then introduce the ItaGen benchmark, which includes a broad range of natural language understanding and generation tasks for Italian, and use it to evaluate the performance of IT5 models and multilingual baselines. We find monolingual IT5 models to provide the best scale-to-performance ratio across tested models, consistently outperforming their multilingual counterparts and setting a new state-of-the-art for Italian language generation.",
}