model_cards/article.md

# Model documentation & parameters

**Language model**: Type of language model to be used.

**Prefix**: Task specific prefix for task definition (see the provided examples for specific tasks).

**Text prompt**: The text input of the model.

**Num beams**: Number of beams to be used for the text generation.



# Model card -- PatentGenerativeTransformer

**Model Details**: Text+chem 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)

**Developers**: Dimitrios Christofidellis, Giorgio Giannone, Jannis Born and Matteo Manica from IBM Research and Ole Winther from Technical University of Denmark.

**Distributors**: Model natively integrated into GT4SD.

**Model date**: 2022.

**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 Text+chem 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).

**Intended Use. Use cases that were envisioned during development**: N.A.

**Primary intended uses/users**: N.A.

**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.

**Metrics**: N.A.

**Datasets**: N.A.

**Ethical Considerations**: Unclear, please consult with original authors in case of questions.

**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.

Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)

## 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}
}
```