Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-classification
+- text-generation
+- text2text-generation
+- text-retrieval
+language:
+- en
+tags:
+- Coder-Stat
+- c++
+- C
+- Python
+- Java
+- Go
+- Ruby
+- R
+- c#
+size_categories:
+- 10K<n<100K
+---
+
+# Coder-Stat Dataset
+
+## Overview
+The Coder-Stat dataset is a collection of programming-related data, including problem IDs, programming languages, original statuses, and source code snippets. This dataset is designed to assist in the analysis of coding patterns, error types, and performance metrics.
+
+## Dataset Details
+
+### Modalities
+- **Tabular**: The dataset is structured in a tabular format.
+- **Text**: Contains text data, including source code snippets.
+
+### Formats
+- **JSON**: The dataset is available in JSON format.
+
+### Size
+- The dataset contains between 10,000 to 100,000 entries.
+
+### Libraries
+- **Datasets**: The dataset is compatible with the Hugging Face Datasets library.
+- **Pandas**: Can be easily loaded and manipulated using Pandas.
+- **Croissant**: Additional support for Croissant format.
+
+### License
+- **Apache-2.0**: The dataset is licensed under the Apache License 2.0.
+
+## Dataset Structure
+
+### Columns
+- **problem_id**: A unique identifier for each problem.
+- **language**: The programming language used.
+- **original_status**: The original status of the code (e.g., Runtime Error, Time Limit Exceeded).
+- **original_sre**: The original source code.
+- **changed_sre**: The modified or corrected source code.
+
+### Splits
+- **train**: Contains 41,900 rows of training data.
+
+## Usage
+
+### Loading the Dataset
+You can load the dataset using the Hugging Face Datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("prithivMLmods/Coder-Stat")
+```
+
+### Example
+Here’s an example of how to access the data:
+
+```python
+import pandas as pd
+
+# Load the dataset
+dataset = load_dataset("prithivMLmods/Coder-Stat")
+
+# Convert to Pandas DataFrame
+df = pd.DataFrame(dataset['train'])
+
+# Display the first few rows
+print(df.head())
+```