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