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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: "maronn qualcuno mi spieg' CHECCOSA SUCCEDE?!?!" |
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- text: "wellaaaaaaa, ma fraté sei proprio troppo simpatiko, grazieeee!!" |
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- text: "nn capisco xke tt i ragazzi lo fanno" |
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- text: "IT5 è SUPERMEGA BRAVISSIMO a capire tt il vernacolo italiano!!!" |
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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-small-informal-to-formal |
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results: |
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- task: |
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type: formality-style-transfer |
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name: "Informal-to-formal 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: 0.638 |
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name: "Avg. Test Rouge1" |
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- type: rouge2 |
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value: 0.446 |
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name: "Avg. Test Rouge2" |
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- type: rougeL |
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value: 0.620 |
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name: "Avg. Test RougeL" |
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- type: bertscore |
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value: 0.684 |
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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: "17g" |
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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 Small for Informal-to-formal Style Transfer 🧐 |
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This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on Informal-to-formal 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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i2f = pipeline("text2text-generation", model='it5/mt5-small-informal-to-formal') |
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i2f("nn capisco xke tt i ragazzi lo fanno") |
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>>> [{"generated_text": "non comprendo perché tutti i ragazzi agiscono così"}] |
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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-small-informal-to-formal") |
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model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-informal-to-formal") |
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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 2203.03759}, |
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url={https://arxiv.org/abs/2203.03759}, |
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year={2022}, |
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month={mar} |
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} |
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``` |
