Update README.md

README.md

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 license: mit
+language:
+- en
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+
+# Multitask Text and Chemistry T5
+
+Multitask Text and Chemistry T5 : a multi-domain, multi-task language model to solve a wide range of tasks in both the chemical and natural language domains. Published by [Christofidellis et al.](https://arxiv.org/pdf/2301.12586.pdf)
+
+**Model Details**: The Multitask Text and Chemistry T5 variant trained using <em>t5-base</em> as its pretrained based and the <em>standard dataset</em>. 
+
+**Developers**: Dimitrios Christofidellis*, Giorgio Giannone*, Jannis Born, Teodoro Laino and Matteo Manica from IBM Research and Ole Winther from Technical University of Denmark.
+
+**Distributors**: Model natively integrated into GT4SD.
+
+**Model date**: 2023.
+
+**Model type**: A Transformer-based language model that is trained on a multi-domain and a multi-task dataset by aggregating available datasets
+for the tasks of Forward reaction prediction, Retrosynthesis, Molecular captioning, Text-conditional de novo generation and Paragraph to actions. 
+
+**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
+N.A.
+
+**Paper or other resource for more information**: 
+The Multitask Text and Chemistry T5 [Christofidellis et al.](https://arxiv.org/pdf/2301.12586.pdf)
+
+
+**License**: MIT
+
+**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).
+
+## Citation
+```bib
+@article{christofidellis2023unifying,
+  title={Unifying Molecular and Textual Representations via Multi-task Language Modelling},
+  author={Christofidellis, Dimitrios and Giannone, Giorgio and Born, Jannis and Winther, Ole and Laino, Teodoro and Manica, Matteo},
+  journal={arXiv preprint arXiv:2301.12586},
+  year={2023}
+}
+```
+
+*equal contribution