Update README.md

README.md

@@ -62,69 +62,26 @@ then, from within python load the datasets library
    
 To load structures from the entire `SAbDab` dataset, use `datasets.load_dataset(...)`:
 
-    >>> dataset_tag = "train"
-    >>> dataset_models = datasets.load_dataset(
-      path = "ProteinMPNN/SAbDab_raw",
-      name = f"{dataset_tag}_models",
-      data_dir = f"{dataset_tag}")['train']
+    >>> SAbDab= datasets.load_dataset("RosettaCommons/SAbDab")
+    Downloading readme: 7.87kB [00:00, 2.73MB/s]
+    Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 15.7M/15.7M [00:01<00:00, 8.22MB/s]
+    Generating train split: 100%|████████████████████████████████████████████████████████████████████████████████| 20700/20700 [00:00<00:00, 80378.32 examples/s]
 
 and the dataset is loaded as a `datasets.arrow_dataset.Dataset`
 
-    >>> dataset_models
-    Dataset({
-        features: [
-          'pdb',
-          'Hchain',
-          'Lchain',
-          'model',
-          'antigen_chain',
-          'antigen_type',
-          'antigen_het_name',
-          'antigen_name',
-          'short_header',
-          'date',
-          'compound',
-          'organism',
-          'heavy_species',
-          'light_species',
-          'antigen_species',
-          'authors',
-          'resolution',
-          'method',
-          'r_free',
-          'r_factor',
-          'scfv',
-          'engineered',
-          'heavy_subclass',
-          'light_subclass',
-          'light_ctype',
-          'affinity',
-          'delta_g',
-          'affinity_method',
-          'temperature',
-          'pmid'
-          'abangle',
-          'annotation_H',
-          'annotation_L',
-          'imgt_H',
-          'imgt_L',
-          'sequence_raw',
-          'sequence_H',
-          'sequence_L',
-          'structure',
-          'structure_chothia',
-          'in_nr_set',
-          'curated_quality_dataset',
-          'split'
-          ],
-        num_rows: 20701
+    >>> SAbDab
+    DatasetDict({
+    train: Dataset({
+        features: ['pdb', 'Hchain', 'Lchain', 'model', 'antigen_chain', 'antigen_type', 'antigen_het_name', 'antigen_name', 'short_header', 'date', 'compound', 'organism', 'heavy_species', 'light_species', 'antigen_species', 'authors', 'resolution', 'method', 'r_free', 'r_factor', 'scfv', 'engineered', 'heavy_subclass', 'light_subclass', 'light_ctype', 'affinity', 'delta_g', 'affinity_method', 'temperature', 'pmid', 'abangle', 'annotation_H', 'annotation_L', 'imgt_H', 'imgt_L', 'sequence_raw', 'sequence_H', 'sequence_L', 'structure', 'structure_chothia', 'in_nr_set', 'curated_quality_dataset', 'split'],
+        num_rows: 20700
+        })
     })
 
 which is a column oriented format that can be accessed directly, converted in to a `pandas.DataFrame`, or `parquet` format, e.g.
 
-    >>> dataset_models.data.column('pdb')
-    >>> dataset_models.to_pandas()
-    >>> dataset_models.to_parquet("dataset.parquet")
+    >>> SAbDab.data.column('pdb')
+    >>> SAbDab.to_pandas()
+    >>> SAbDab.to_parquet("dataset.parquet")
 
 ## Dataset Details