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--- |
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language: |
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- it |
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license: apache-2.0 |
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tags: |
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- italian |
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- sequence-to-sequence |
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- style-transfer |
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- formality-style-transfer |
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datasets: |
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- yahoo/xformal_it |
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widget: |
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- text: "Questa performance è a dir poco spiacevole." |
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- text: "In attesa di un Suo cortese riscontro, Le auguriamo un piacevole proseguimento di giornata." |
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- text: "Questa visione mi procura una goduria indescrivibile." |
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- text: "qualora ciò possa interessarti, ti pregherei di contattarmi." |
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metrics: |
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- rouge |
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- bertscore |
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model-index: |
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- name: mt5-base-formal-to-informal |
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results: |
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- task: |
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type: formality-style-transfer |
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name: "Formal-to-informal Style Transfer" |
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dataset: |
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type: xformal_it |
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name: "XFORMAL (Italian Subset)" |
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metrics: |
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- type: rouge1 |
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value: 0.653 |
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name: "Avg. Test Rouge1" |
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- type: rouge2 |
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value: 0.449 |
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name: "Avg. Test Rouge2" |
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- type: rougeL |
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value: 0.632 |
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name: "Avg. Test RougeL" |
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- type: bertscore |
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value: 0.667 |
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name: "Avg. Test BERTScore" |
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args: |
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- model_type: "dbmdz/bert-base-italian-xxl-uncased" |
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- lang: "it" |
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- num_layers: 10 |
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- rescale_with_baseline: True |
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- baseline_path: "bertscore_baseline_ita.tsv" |
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co2_eq_emissions: |
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emissions: "40g" |
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source: "Google Cloud Platform Carbon Footprint" |
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training_type: "fine-tuning" |
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geographical_location: "Eemshaven, Netherlands, Europe" |
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hardware_used: "1 TPU v3-8 VM" |
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--- |
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# mT5 Base for Formal-to-informal Style Transfer 🤗 |
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This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/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](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). |
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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. |
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## Using the model |
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Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: |
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```python |
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from transformers import pipelines |
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f2i = pipeline("text2text-generation", model='it5/mt5-base-formal-to-informal') |
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f2i("Vi ringrazio infinitamente per vostra disponibilità") |
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>>> [{"generated_text": "e grazie per la vostra disponibilità!"}] |
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``` |
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or loaded using autoclasses: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("it5/mt5-base-formal-to-informal") |
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model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-base-formal-to-informal") |
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``` |
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If you use this model in your research, please cite our work as: |
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```bibtex |
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@article{sarti-nissim-2022-it5, |
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title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, |
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author={Sarti, Gabriele and Nissim, Malvina}, |
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journal={ArXiv preprint TBD}, |
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url={TBD}, |
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year={2022} |
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} |
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``` |
