Update README.md

README.md

@@ -685,6 +685,32 @@ answering systems on tabular data, they are not large and diverse enough to eval
 To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1615 columns in total, and 1300 questions to evaluate your models for the task of QA over Tabular Data.
 
 
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load all QA pairs
+all_qa = load_dataset("cardiffnlp/databench", name="qa", split="full")
+
+# Load SemEval 2025 task 8 Question-Answer splits
+semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train")
+semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev")
+
+
+# "001_Forbes", the id of the dataset where information to answer the Question is located
+all_qa['dataset'][0] 
+
+# This id can be used load a specific Question-Answer pair collection from the splits
+forbes_qa = load_dataset("cardiffnlp/databench", name="qa", split=all_qa['dataset'][0] )
+
+# you can load a specific dataset containg the "answer" for a QA pair using the 
+forbes_full = load_dataset("cardiffnlp/databench", name=all_qa['dataset'][0] , split="full")
+
+# or to load the databench lite equivalent dataset, to answer the "sample_answer"
+forbes_sample = load_dataset("cardiffnlp/databench", name=all_qa['dataset'][0] , split="lite")
+```
+
 ## 📚 Datasets
 By clicking on each name in the table below, you will be able to explore each dataset.
 
@@ -797,30 +823,4 @@ If you use this resource, please use the following reference:
     year = "2024",
     address = "Turin, Italy"
 }
-```
-
-# Usage
-
-```python
-from datasets import load_dataset
-
-# Load all QA pairs
-all_qa = load_dataset("cardiffnlp/databench", name="qa", split="full")
-
-# Load SemEval 2025 task 8 Question-Answer splits
-semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train")
-semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev")
-
-
-# is "001_Forbes", the id of the dataset where information to answer the Question is located
-all_qa['dataset'][0] 
-
-# This id can be used load a specific Question-Answer pair collection from the splits
-forbes_qa = load_dataset("cardiffnlp/databench", name="qa", split=all_qa['dataset'][0] )
-
-# you can load a specific dataset containg the "answer" for a QA pair using the 
-forbes_full = load_dataset("cardiffnlp/databench", name=all_qa['dataset'][0] , split="full")
-
-# or to load the databench lite equivalent dataset, to answer the "sample_answer"
-forbes_sample = load_dataset("cardiffnlp/databench", name=all_qa['dataset'][0] , split="lite")
 ```