update

app.py

@@ -59,7 +59,7 @@ if __name__ == "__main__":
 
     demo = gr.Interface(
         fn=run_inference,
-        title=TITLE,
+        title="MoLeR (MOlecule-LEvel Representation)",
         inputs=[
             gr.Dropdown(algos, label="Algorithm version", value="v0"),
             gr.Textbox(

model_cards/article.md

@@ -1,4 +1,17 @@
-### Model card - MoLeR
+# Model documentation & parameters
+
+**Algorithm Version**: Which model checkpoint to use (trained on different datasets).
+
+**Scaffolds**: One or multiple scaffolds (or seed molecules), provided as '.'-separated SMILES. If empty, no scaffolds are used.
+
+**Number of samples**: How many samples should be generated (between 1 and 50).
+
+**Beam size**: Beam size used in beam search decoding (the higher the slower but better).
+
+**Seed**: The random seed used for initialization.
+
+
+# Model card - MoLeR
 
 **Model Details**: MoLeR is a graph-based molecular generative model that can be conditioned (primed) on scaffolds. The model decorates scaffolds with realistic structural motifs.
 
@@ -36,7 +49,7 @@
 
 **Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
 
-Model card prototype inspired by [*Mitchell et al. (2019),  Proceedings of the Conference on Fairness, Accountability, and Transparency*](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
+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
 

model_cards/description.md

@@ -1,23 +1,6 @@
+<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
 
-# MoLeR (MOlecule-LEvel Representation)
-
-<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="80" >
 This model is provided and distributed by the **GT4SD** (Generative Toolkit for Scientific Discovery).
 
-
-## Model documentation & parameters
-
-### Algorithm Version:
-Which model checkpoint to use (trained on different datasets).
-
-### Scaffolds
-One or multiple scaffolds (or seed molecules), provided as '.'-separated SMILES. If empty, no scaffolds are used.
-
-### Number of samples:
-How many samples should be generated (between 1 and 50). 
-
-### Beam size
-Beam size used in beam search decoding (the higher the slower but better).
-
-### Seed
-The random seed used for initialization.
+For **examples** and **documentation** of the model parameters, please see below.
+Moreover, we provide **model cards** ([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)) with details of the model at the bottom of this page.