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model_cards/regression_transformer_article.md

@@ -57,7 +57,7 @@ Optionally specifies a list of substructures that should definitely be present i
 
 **Model date**: Preprint released in 2022, currently under review at *Nature Machine Intelligence*.
 
-**Model version**: Models trained and distributed by the original authors.
+**Algorithm version**: Models trained and distributed by the original authors.
 - **Molecules: QED**: Model trained on 1.6M molecules (SELFIES) from ChEMBL and their QED scores.
 - **Molecules: Solubility**: QED model finetuned on the ESOL dataset from [Delaney et al (2004), *J. Chem. Inf. Comput. Sci.*](https://pubs.acs.org/doi/10.1021/ci034243x) to predict water solubility. Model trained on augmented SELFIES.
 - **Molecules: USPTO**: Model trained on 2.8M [chemical reactions](https://figshare.com/articles/dataset/Chemical_reactions_from_US_patents_1976-Sep2016_/5104873) from the US patent office. The model used SELFIES and a synthetic property (total molecular weight of all precursors).
@@ -88,9 +88,9 @@ The [Regression Transformer](https://arxiv.org/abs/2202.01338) paper. See the [s
 
 **Factors**: Not applicable.
 
-**Metrics**: High predictive power for the properties of the `model_version`.
+**Metrics**: High predictive power for the properties of that specific algorithm version.
 
-**Datasets**: Different ones, as described under **Model version**.
+**Datasets**: Different ones, as described under **Algorithm version**.
 
 **Ethical Considerations**: No specific considerations as no private/personal data is involved. Please consult with the authors in case of questions.