Update README.md

README.md

@@ -145,30 +145,62 @@ For detailed information, please read our paper and code.
 
 ## How to Use
 
-- Acounting fraud detection
+**Acounting fraud detection**
 
 This task is a binary classification problem aimed at predicting whether a given annual report is fraudulent.
 The label is either fraud (1) or non-fraud (0).
 
 ```python
-ds = load_dataset("SakanaAI/EDINET-Bench", "fraud_detection")
+>>> from datasets import load_dataset
+>>> ds = load_dataset("SakanaAI/EDINET-Bench", "fraud_detection")
+>>> ds
+DatasetDict({
+    train: Dataset({
+        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'explanation', 'edinet_code', 'ammended_doc_id', 'doc_id', 'file_path'],
+        num_rows: 865
+    })
+    test: Dataset({
+        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'explanation', 'edinet_code', 'ammended_doc_id', 'doc_id', 'file_path'],
+        num_rows: 224
+    })
+})
 ```
 
-- Earnings forecast
+**Earnings forecast**
 
 This task is a binary classification problem that predicts whether a company's earnings will increase or decrease in the next fiscal year based on its current annual report.
 The label is either increase (1) or not (0).
 ```python
-ds = load_dataset("SakanaAI/EDINET-Bench", "earnings_forecast")
+>>> from datasets import load_dataset
+>>> ds = load_dataset("SakanaAI/EDINET-Bench", "earnings_forecast")
+>>> ds
+DatasetDict({
+    train: Dataset({
+        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'naive_prediction', 'edinet_code', 'doc_id', 'previous_year_file_path', 'current_year_file_path'],
+        num_rows: 549
+    })
+    test: Dataset({
+        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'label', 'naive_prediction', 'edinet_code', 'doc_id', 'previous_year_file_path', 'current_year_file_path'],
+        num_rows: 451
+    })
+})
 ```
 
-- Industry prediction
+**Industry prediction**
 
 This task is a multi-class classification problem that predicts a company's industry type (e.g., Banking) based on its current annual report.
-Each label represents one of 16 possible industry types.
+Each label (in this case, the industry column) represents one of 16 possible industry types.
 
 ```python
-ds = load_dataset("SakanaAI/EDINET-Bench", "industry_prediction")
+>>> from datasets import load_dataset
+>>> ds = load_dataset("SakanaAI/EDINET-Bench", "industry_prediction")
+>>> ds
+DatasetDict({
+    train: Dataset({
+        features: ['meta', 'summary', 'bs', 'pl', 'cf', 'text', 'industry', 'edinet_code', 'doc_id', 'file_path'],
+        num_rows: 496
+    })
+})
 ```
 
 ## LICENSE