Update README.md

README.md

@@ -35,10 +35,24 @@ The length of each text sample was calculated in terms of tokens using the T5 to
 
 The resulting processed data files are stored in Apache parquet and can be loaded using the `pandas' library or the `datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are
 
-- `article`: the scientific article text (string) 
-- `summary`: the lay summary text (string) 
-- `article_length`: the length of the article in tokens (int) 
-- `summary_length`: the length of the summary in tokens (int)
+
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
+        num_rows: 24773
+    })
+    test: Dataset({
+        features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
+        num_rows: 1376
+    })
+    validation: Dataset({
+        features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
+        num_rows: 1376
+    })
+})
+```
 ## Usage
 
 Load the desired parquet file(s) using `pandas` or `datasets`. Here is an example using `pandas`:
@@ -63,5 +77,5 @@ dataset = load_dataset("pszemraj/scientific_lay_summarisation-plos-norm")
 train_set = dataset['train']
 # Print the first few samples
 for i in range(5):
-    print(dataset[i])
+    print(train_set[i])
 ```