Update README.md

README.md

@@ -140,7 +140,7 @@ then, from within python load the datasets library
    
 and load one of the `AttentiveSkin` datasets, e.g.,
 
-    >>> AttentiveSkin = datasets.load_dataset("maomlab/AttentiveSkin", name = "AttentiveSkin")
+    >>> Corr_Neg = datasets.load_dataset("maomlab/AttentiveSkin", name = 'Corr_Neg')
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@@ -149,18 +149,18 @@ and load one of the `AttentiveSkin` datasets, e.g.,
    
 and inspecting the loaded dataset
 
-    >>> AttentiveSkin
-    AttentiveSkin
+    >>> Corr_Neg
     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
+    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'],
+        num_rows: 181
     })
-        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'],
+        num_rows: 1755
     })
-  })
+})
+
 
 
 ### Use a dataset to train a model