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README.md

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+---
+license: mit
+task_categories:
+- tabular-classification
+tags:
+- tabular
+- classification
+- student-grades
+- label-errors
+- noisy-labels
+- data-centric-ai
+---
+
+# Student Grades Demo Dataset
+
+This dataset contains student grades data with both true labels and noisy (corrupted) labels.
+
+## Dataset Description
+
+The dataset includes:
+- Student exam scores (exam_1, exam_2, exam_3)
+- Notes field
+- True letter grades (`letter_grade`)
+- Noisy/corrupted letter grades (`noisy_letter_grade`)
+
+This is useful for demonstrating and validating label error detection methods.
+
+## Usage
+
+```python
+import pandas as pd
+
+# Load the dataset
+df = pd.read_csv("hf://datasets/Cleanlab/student-grades-demo/student-grades-demo.csv")
+print(df.head())
+
+# Find rows where labels were corrupted
+import numpy as np
+true_errors = np.where(df["letter_grade"] != df["noisy_letter_grade"])[0]
+print(f"Number of label errors: {len(true_errors)}")
+```
+
+## License
+
+MIT License