Added example datasets usage

README.md

@@ -24,6 +24,51 @@ figure given a caption. This benchmark was curated from the SciCap dataset, usin
 on each question to ensure high-quality, 
 answerable questions.
 
+### Example Usage
+```python
+from datasets import load_dataset
+
+# load dataset
+dataset = load_dataset("jonathan-roberts1/SciFIBench") # optional: set cache_dir="PATH/TO/MY/CACHE/DIR"
+# figure2caption_dataset = load_dataset("jonathan-roberts1/SciFIBench", split="Figure2Caption")
+# caption2figure_dataset = load_dataset("jonathan-roberts1/SciFIBench", split="Caption2Figure")
+"""
+DatasetDict({
+    Caption2Figure: Dataset({
+        features: ['ID', 'Question', 'Options', 'Answer', 'Category', 'Images'],
+        num_rows: 500
+    })
+    Figure2Caption: Dataset({
+        features: ['ID', 'Question', 'Options', 'Answer', 'Category', 'Images'],
+        num_rows: 500
+    })
+})
+"""
+
+# select task
+figure2caption_dataset = dataset['Figure2Caption']
+"""
+Dataset({
+    features: ['ID', 'Question', 'Options', 'Answer', 'Category', 'Images'],
+    num_rows: 500
+})
+"""
+
+# query items
+figure2caption_dataset[40] # e.g., the 41st element
+"""
+{'ID': 40,
+ 'Question': 'Which caption best matches the image?',
+ 'Options': ['A)  ber vs snr for fft size=2048 using ls , lmmse , lr-lmmse .',
+  'B)  ber vs snr for fft size=1024 using ls , lmmse , lr-lmmse algorithms .',
+  'C)  ber vs snr for fft size=512 using ls , lmmse , lr-lmmse algorithms .',
+  'D)  ber vs snr for fft size=256 using ls , lmmse , lr-lmmse algorithms with a 16 qam modulation .',
+  'E)  ber vs snr for a bpsk modulation .'],
+ 'Answer': 'D',
+ 'Category': 'other cs',
+ 'Images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=501x431>]}
+"""
+```
 
 ### Source Data