Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: stud_ID
-    dtype: string
-  - name: exam_1
-    dtype: float64
-  - name: exam_2
-    dtype: float64
-  - name: exam_3
-    dtype: float64
-  - name: notes
-    dtype: string
-  - name: noisy_letter_grade
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 35289
-    num_examples: 615
-  - name: test
-    num_bytes: 7689
-    num_examples: 134
-  download_size: 15661
-  dataset_size: 42978
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: mit
+task_categories:
+- tabular-classification
+language:
+- en
+tags:
+- education
+- data-centric-ai
+- label-noise
+- cleanlab
+pretty_name: Student Grades Dataset
+size_categories:
+- n<1K
 ---
+
+# Student Grades Dataset
+
+## Dataset Description
+
+This dataset contains student grade data used in the cleanlab tutorial: [Improving ML Performance via Data Curation with Train vs Test Splits](https://docs.cleanlab.ai/stable/tutorials/improving_ml_performance.html).
+
+The task is to predict each student's final letter grade (A, B, C, D, F) based on their exam scores and notes.
+
+### Dataset Summary
+
+- **Total Examples**: ~750 (train + test)
+- **Task**: Multi-class classification
+- **Features**:
+  - `exam_1`: Score on first exam (0-100)
+  - `exam_2`: Score on second exam (0-100)
+  - `exam_3`: Score on third exam (0-100)
+  - `notes`: Categorical notes about student (e.g., "great participation +10", "cheated on exam, gets 0pts")
+  - `stud_ID`: Unique student identifier
+- **Label**: `noisy_letter_grade` - Letter grade (A, B, C, D, F)
+
+### Dataset Structure
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("cleanlab/student-grades")
+
+# Access splits
+train_data = dataset["train"]
+test_data = dataset["test"]
+
+# Convert to pandas
+import pandas as pd
+df_train = train_data.to_pandas()
+df_test = test_data.to_pandas()
+```
+
+### Data Splits
+
+| Split | Examples |
+|-------|----------|
+| train | ~600     |
+| test  | ~130     |
+
+### Dataset Fields
+
+- **stud_ID** (string): Unique student identifier
+- **exam_1** (float): First exam score (0-100)
+- **exam_2** (float): Second exam score (0-100)
+- **exam_3** (float): Third exam score (0-100)
+- **notes** (string): Categorical notes about the student
+- **noisy_letter_grade** (string): Final letter grade (A, B, C, D, F) - may contain label errors
+
+## Dataset Creation
+
+This dataset was created for educational purposes to demonstrate data-centric AI techniques using cleanlab. The data intentionally contains:
+- **Label noise**: Some grades may be incorrectly labeled
+- **Near duplicates**: Some examples are very similar or exact duplicates
+- **Outliers**: Unusual data points that don't fit the distribution
+
+These issues are introduced to help users learn how to detect and handle common data quality problems using cleanlab.
+
+## Uses
+
+### Primary Use Case
+
+This dataset is designed for:
+1. Learning data-centric AI techniques
+2. Demonstrating cleanlab's capabilities for detecting label errors, outliers, and near duplicates
+3. Teaching proper train/test data curation workflows
+
+### Example Usage
+
+```python
+from datasets import load_dataset
+from cleanlab import Datalab
+
+# Load dataset
+dataset = load_dataset("cleanlab/student-grades")
+df_train = dataset["train"].to_pandas()
+
+# Use cleanlab to detect issues
+lab = Datalab(data=df_train, label_name="noisy_letter_grade", task="classification")
+lab.find_issues()
+lab.report()
+```
+
+## Tutorial
+
+For a complete tutorial using this dataset, see:
+[Improving ML Performance via Data Curation with Train vs Test Splits](https://docs.cleanlab.ai/stable/tutorials/improving_ml_performance.html)
+
+## Licensing Information
+
+MIT License
+
+## Citation
+
+If you use this dataset in your research, please cite the cleanlab library:
+
+```bibtex
+@software{cleanlab,
+  author = {Northcutt, Curtis G. and Athalye, Anish and Mueller, Jonas},
+  title = {cleanlab},
+  year = {2021},
+  url = {https://github.com/cleanlab/cleanlab},
+}
+```
+
+## Contact
+
+- **Maintainers**: Cleanlab Team
+- **Repository**: https://github.com/cleanlab/cleanlab
+- **Documentation**: https://docs.cleanlab.ai
+- **Issues**: https://github.com/cleanlab/cleanlab/issues