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.
Model Details: The Multitask Text and Chemistry T5 variant trained using t5-small as its pretrained based and the augmented dataset.
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.(2023)
License: MIT
Where to send questions or comments about the model: Open an issue on GT4SD repository.
Citation
@inproceedings{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},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
pages = {6140--6157},
year = {2023},
volume = {202},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v202/christofidellis23a/christofidellis23a.pdf},
url = {https://proceedings.mlr.press/v202/christofidellis23a.html},
}
*equal contribution
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