Create README.md

README.md

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+---
+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: mt5-base-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: 0.806
+        name: "Avg. Test Rouge1"
+      - type: rouge2
+        value: 0.685
+        name: "Avg. Test Rouge2"
+      - type: rougeL
+        value: 0.796
+        name: "Avg. Test RougeL"
+      - type: bertscore
+        value: 0.818
+        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: "40g"
+      source: "Google Cloud Platform Carbon Footprint"
+      training_type: "fine-tuning"
+      geographical_location: "Eemshaven, Netherlands, Europe"
+      hardware_used: "1 TPU v3-8 VM"
+---
+
+# mT5 Base for Formal-to-informal Style Transfer 🤗
+
+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) by Gabriele Sarti and Malvina Nissim. 
+
+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/mt5-base-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/mt5-base-formal-to-informal")
+model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-base-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 TBD},
+    url={TBD},
+    year={2022}
+}
+```