Create README.md

README.md

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+---
+license: gpl-3.0
+language:
+- en
+widget:
+- text: "Sentence: We tried 4 different styles of donuts. The distribution of numerals in the sentence is equal to"
+  example_title: "Example 1"
+- text: "Sentence: No one's going to take you seriously if they're full of typos. The distribution of subordinates in the sentence is equal to"
+  example_title: "Example 2"
+---
+
+# Li-IT5 Small
+
+<p align="center">
+    <img src="lit5.png" alt="Linguistically-Informed T5" width="500"/>
+</p>
+
+
+This model is released as part of the paper "Linguistic Knowledge Can Enhance Encoder-Decoder Models (*If You Let It*)" (Miaschi et al., 2024). 
+If you use this model in your work, we kindly ask you to cite our paper:
+
+```bibtex
+@inproceedings{miaschi_linguistic_knowledge,
+    title = "Linguistic Knowledge Can Enhance Encoder-Deocer Models (If You Let It)",
+    author = "Miaschi, Alessio and Dell'Orletta Felice and Venturi, Giulia",
+}
+```
+
+Other information can be found in the original [GitHub repository](https://github.com/alemiaschi/linguistically_informed_t5/tree/main).
+
+## Model Description
+
+The model is based on a T5 model fine-tuned in a multitask fashion to solve a set of raw, morpho-syntactic and syntactic tasks (i.e. predictions of linguistic properties). 
+The full list of the 10 linguistic properties used as intermediate tasks can be found in the original paper.
+
+This model is based on the English version of t5-small, [t5-small](https://huggingface.co/google-t5/t5-small).
+
+## Model variations
+
+The other fine-tuned models presented in the original study are the following:
+
+- [li-it5-small](https://huggingface.co/alemiaschi/li-it5-small)
+- [li-it5-base](https://huggingface.co/alemiaschi/li-it5-base)