readme: ---
  language: "it"
  license: mit
  datasets:
  - gsarti/clean_mc4_it
  tags:
  - bart
  - pytorch
  pipeline:
  - text2text-generation
  ---
  
  # BART-IT: Italian pretraining for BART sequence to sequence model
  
  BART-IT is a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a [large corpus of Italian text](https://huggingface.co/datasets/gsarti/clean_mc4_it), and can be fine-tuned on a variety of tasks.
  
  ## Model description
  
  The model is a `base-`sized BART model, with a vocabulary size of 52,000 tokens. It has 140M parameters and can be used for any task that requires a sequence-to-sequence model. It is trained from scratch on a large corpus of Italian text, and can be fine-tuned on a variety of tasks.
  
  
  ## Pre-training
  
  The code used to pre-train BART-IT together with additional information on model parameters can be found [here](https://github.com/MorenoLaQuatra/bart-it).
  
  ## Fine-tuning
  
  The model in this repository is a pre-trained model without any fine-tuning. In order to use the model for a specific task, you can fine-tune it on a specific dataset.
  
  The model has been fine-tuned for the abstractive summarization task on 3 different Italian datasets:
  
  - [FanPage](https://huggingface.co/datasets/ARTeLab/fanpage) - finetuned model [here](https://huggingface.co/morenolq/bart-it-fanpage)
  - [IlPost](https://huggingface.co/datasets/ARTeLab/ilpost) - finetuned model [here](https://huggingface.co/morenolq/bart-it-ilpost)
  - [WITS](https://huggingface.co/datasets/Silvia/WITS) - finetuned model [here](https://huggingface.co/morenolq/bart-it-WITS)
  
  ## Usage
  
  In order to use the model, you can use the following code:
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("morenolq/bart-it")
  model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/bart-it")
  
  input_ids = tokenizer.encode("Il modello BART-IT è stato pre-addestrato su un corpus di testo italiano", return_tensors="pt")
  outputs = model.generate(input_ids, max_length=40, num_beams=4, early_stopping=True)
  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
  ```
  
  # Citation
  
  If you find this model useful for your research, please cite the following paper:
  
  ```bibtex
  @Article{BARTIT,
  	AUTHOR = {La Quatra, Moreno and Cagliero, Luca},
  	TITLE = {BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization},
  	JOURNAL = {Future Internet},
  	VOLUME = {15},
  	YEAR = {2023},
  	NUMBER = {1},
  	ARTICLE-NUMBER = {15},
  	URL = {https://www.mdpi.com/1999-5903/15/1/15},
  	ISSN = {1999-5903},
  	DOI = {10.3390/fi15010015}
  }
  ```
  