readme: ---
  language:
  - it
  license: apache-2.0
  tags:
  - italian
  - sequence-to-sequence
  - style-transfer
  - formality-style-transfer
  datasets:
  - yahoo/xformal_it
  widget:
  - text: "Questa performance è a dir poco spiacevole."
  - text: "In attesa di un Suo cortese riscontro, Le auguriamo un piacevole proseguimento di giornata."
  - text: "Questa visione mi procura una goduria indescrivibile."
  - text: "qualora ciò possa interessarti, ti pregherei di contattarmi."
  metrics:
  - rouge
  - bertscore
  model-index:
  - name: it5-large-formal-to-informal
    results:
    - task: 
        type: formality-style-transfer
        name: "Formal-to-informal Style Transfer"
      dataset:
        type: xformal_it
        name: "XFORMAL (Italian Subset)"
      metrics:
        - type: rouge1
          value: 0.611
          name: "Avg. Test Rouge1"
        - type: rouge2
          value: 0.409
          name: "Avg. Test Rouge2"
        - type: rougeL
          value: 0.586
          name: "Avg. Test RougeL"
        - type: bertscore
          value: 0.613
          name: "Avg. Test BERTScore"
          args:
            - model_type: "dbmdz/bert-base-italian-xxl-uncased"
            - lang: "it"
            - num_layers: 10
            - rescale_with_baseline: True
            - baseline_path: "bertscore_baseline_ita.tsv"
  co2_eq_emissions:
        emissions: "51g"
        source: "Google Cloud Platform Carbon Footprint"
        training_type: "fine-tuning"
        geographical_location: "Eemshaven, Netherlands, Europe"
        hardware_used: "1 TPU v3-8 VM"
  ---
  
  # IT5 Large for Formal-to-informal Style Transfer 🤗
  
  This repository contains the checkpoint for the [IT5 Large](https://huggingface.co/gsarti/it5-large) 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](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). 
  
  A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/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/it5-large-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/it5-large-formal-to-informal")
  model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-large-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 2203.03759},
      url={https://arxiv.org/abs/2203.03759},
      year={2022},
  	month={mar}
  }
  ```