
# 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
{"language": ["en"], "license": "mit"}
transformers pytorch safetensors t5 text2text-generation en arxiv:2301.12586 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #arxiv-2301.12586 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# 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 <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.


License: MIT

Where to send questions or comments about the model: Open an issue on GT4SD repository.

*equal contribution
# 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 <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.


License: MIT

Where to send questions or comments about the model: Open an issue on GT4SD repository.

*equal contribution
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #arxiv-2301.12586 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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 <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.


License: MIT

Where to send questions or comments about the model: Open an issue on GT4SD repository.

*equal contribution