update

model_cards/regression_transformer_article.md

@@ -103,12 +103,19 @@ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi
 ## Citation
 
 ```bib
-@article{born2022regression,
-  title={Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens},
+@article{born2023regression,
+  title={Regression Transformer enables concurrent sequence regression and generation for molecular language modelling},
   author={Born, Jannis and Manica, Matteo},
-  journal={arXiv preprint arXiv:2202.01338},
-  note={Spotlight talk at ICLR workshop on Machine Learning for Drug Discovery},
-  year={2022}
+  journal={Nature Machine Intelligence},
+  year={2023},
+  month={04},
+  day={06},
+  volume={},
+  number={},
+  pages={},
+  note={},
+  doi={10.1038/s42256-023-00639-z},
+  url={https://doi.org/10.1038/s42256-023-00639-z},
 }
 ```
 

model_cards/regression_transformer_description.md

@@ -4,8 +4,8 @@
 
 ### Concurrent sequence regression and generation for molecular language modeling
 
-The [Regression Transformer](https://arxiv.org/abs/2202.01338) is a multitask Transformer that reformulates regression as a conditional sequence modeling task.
-This yields a dichotomous language model that seamlessly integrates property prediction with property-driven conditional generation. For details see the [arXiv preprint](https://arxiv.org/abs/2202.01338), the [development code](https://github.com/IBM/regression-transformer) and the [GT4SD endpoint](https://github.com/GT4SD/gt4sd-core) for inference.
+The [Regression Transformer](https://www.nature.com/articles/s42256-023-00639-z) is a multitask Transformer that reformulates regression as a conditional sequence modeling task.
+This yields a dichotomous language model that seamlessly integrates property prediction with property-driven conditional generation. For details see the [*Nature Machine Intelligence* paper](https://www.nature.com/articles/s42256-023-00639-z), the [development code](https://github.com/IBM/regression-transformer) and the [GT4SD endpoint](https://github.com/GT4SD/gt4sd-core) for inference.
 
 Each `algorithm_version` refers to one trained model. Each model can be used for **two tasks**, either to *predict* one (or multiple) properties of a molecule or to *generate* a molecule (given a seed molecule and a property constraint).