ci: upper bound pydantic

examples/regression_transformer/README.md

@@ -19,7 +19,9 @@ To launch a finetuning of a RT pretrained on drug-like molecules from ChEMBL, ex
 *NOTE*: This is *dummy* example, do not use "as is" :warning: Substitute the train/test data path to point to your files. You can inspect the format of the example file to see the needed format (`.csv` with the first column called "text" and one or multiple property columns). Adjust the remaining arguments as desired. See full API [here](https://gt4sd.github.io/gt4sd-core/api/gt4sd.training_pipelines.regression_transformer.core.html).
 
 *NOTE*: Substitute the `--model_path` to the directory of the pretrained model. GT4SD provides RT models pretrained on:
-- **small molecules**: `qed`, `esol`, `crippen_logp` ) or multiple ( `logp_and_synthesizability`, `cosmo_acdl`, `pfas`) properties
+- **small molecules**: 
+  - Models for single properties: `qed`, `esol`, `crippen_logp`
+  - Models for multiple properties: `logp_and_synthesizability`, `cosmo_acdl`, `pfas`
 - **proteins**: `stability`
 - **chemical reactions**: `uspto`
 - **polymers**: `rop_catalyst` and `block_copolymer`, both described in [Park et al., (2023; Nature Communications)](https://www.nature.com/articles/s41467-023-39396-3)

requirements.txt

@@ -19,7 +19,7 @@ nglview>=3.0.3
 numpy>=1.16.5,<1.24.0
 protobuf<3.20
 pyarrow>=8.0.0
-pydantic>=1.7.3
+pydantic>=1.7.3,<2.0.0
 pymatgen>=2022.11.7
 PyTDC==0.3.7
 pytorch_lightning<=1.7.7