Update README.md

README.md

@@ -90,17 +90,18 @@ and inspecting the loaded dataset
 
     >>> AttentiveSkin
     AttentiveSkin
-DatasetDict({
-    test: Dataset({
-        features: ['Name', 'Synonym', 'CAS RN', 'GHS', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split', 'ClusterNo', 'MolCount', 'group'],
-        num_rows: 803
+    DatasetDict({
+        test: Dataset({
+            features: ['Name', 'Synonym', 'CAS RN', 'GHS', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split', 'ClusterNo', 'MolCount', 'group'],
+            num_rows: 803
     })
-    train: Dataset({
-        features: ['Name', 'Synonym', 'CAS RN', 'GHS', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split', 'ClusterNo', 'MolCount', 'group'],
-        num_rows: 2416
+        train: Dataset({
+           features: ['Name', 'Synonym', 'CAS RN', 'GHS', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split', 'ClusterNo', 'MolCount', 'group'],
+           num_rows: 2416
     })
 })
 
+
 ### Use a dataset to train a model
 One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
 First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support