Update README.md

README.md

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   - split: train
     path: data/train-*
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+---
+# Dataset Card for Open Source Code and Unit Tests
+
+## Dataset Details
+
+### Dataset Description
+
+This dataset contains c++ code snippets and their corresponding ground truth unit tests collected from various open-source GitHub repositories. The primary purpose of this dataset is to aid in the development and evaluation of automated testing tools, code quality analysis, and LLM models for test generation.
+
+- **Curated by:** Vaishnavi Bhargava
+- **Language(s):** C++
+
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6658bb3acf5fc31e3a0bd24a/hyIhFHmrjUzypFgNPU2UX.png" alt="image/png" width="800" height="600"/>
+
+## Dataset Structure
+
+```python
+from datasets import Dataset, load_dataset
+
+# Load the dataset
+dataset = load_dataset("Nutanix/cpp_unit_tests_benchmark_dataset")
+
+# View dataset structure
+
+DatasetDict({
+    train: Dataset({
+        features: ['ID', 'Language', 'Repository Name', 'File Name', 'File Path in Repository', 'File Path for Unit Test', 'Code', 'Unit Test - (Ground Truth)'],
+        num_rows: 2653
+    })
+})
+```
+
+The dataset consists of the following columns:
+- `ID`: A unique identifier for each entry in the dataset. [Example: "0"]
+- `Language`: The programming language of the file. [Example: "cpp"]
+- `Repository Name`: The name of the GitHub repository, formatted as organisation/repository. [Example: "google/googletest"]
+- `File Name`: The base name of the file (without extension) where the code or test is located. [Example: "sample1"]
+- `File Path in Repository`: The relative path to the file within the GitHub repository. [Example: "googletest/samples/sample1.cc"]
+- `File Path for Unit Test`: The relative path to the unit test file, if applicable. [Example: "googletest/samples/sample1_unittest.cc"]
+- `Code`: The code content of the file, excluding any documentation or comments.
+- `Unit Test - (Ground Truth)`: The content of the unit test file that tests the code. 
+
+
+### Dataset Sources
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6658bb3acf5fc31e3a0bd24a/jE8b8wf1uV_boMaHxsmnP.png" width="800" height="600" />
+
+- **Repository:** The dataset is sourced from the following GitHub repositories: [Latest Commit before 2 July 24]
+  - [Pytorch](https://github.com/pytorch/pytorch) 
+  - [Abseil Absl](https://github.com/abseil/abseil-cpp) 
+  - [Google Test](https://github.com/google/googletest) 
+  - [Libphonenumber](https://github.com/google/libphonenumber) 
+  - [Tensorstore](https://github.com/google/tensorstore) 
+  - [TensorFlow](https://github.com/tensorflow/tensorflow) 
+  - [Glog](https://github.com/google/glog/tree/master/src/glog) 
+  - [Cel-cpp](https://github.com/google/cel-cpp/tree/master) 
+  - [LevelDB](https://github.com/google/leveldb) 
+  - [Libaddressinput](https://github.com/google/libaddressinput/tree/master) 
+  - [Langsvr](https://github.com/google/langsvr/tree/main)
+  - [tsl](https://github.com/google/tsl.git)
+  - [cel-cpp](https://github.com/google/cel-cpp.git)
+  - [quiche](https://github.com/google/quiche.git)
+
+### Some analysis of the dataset:
+
+
+The box plot representation depicting number of Code and Unit Test lines across different repositories
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6658bb3acf5fc31e3a0bd24a/E7aoKCvyRBjBR89sbetrR.png" width="800" height="600" />
+
+<!-- The histogram visualizes the distribution of the number of lines in the "Code" and "Unit Test-(Ground Truth)" column of the dataset.
+
+<div style="display: flex;">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6658bb3acf5fc31e3a0bd24a/pm9VHIoIJgSBTWcmfXPOO.png" width="300" height="300" style="margin-right: 10px;" />
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6658bb3acf5fc31e3a0bd24a/Fo48OZiHeiVLQZ9yA5qch.png" width="300" height="300" />
+</div>
+
+The histogram visualizes the distribution of the number of tokens in the "Code" and "Unit Test-(Ground Truth)" column of the dataset.
+
+<div style="display: flex;">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6658bb3acf5fc31e3a0bd24a/UWb5i1bh5keq8hd7NdT6E.png" width="300" height="300" style="margin-right: 10px;" />
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6658bb3acf5fc31e3a0bd24a/bAgGzQGmrVrMxm-uHxffv.png" width="300" height="300" />
+</div>
+ -->
+
+## Uses
+
+### Direct Use
+
+This dataset is suitable for :
+- Developing and evaluating automated testing tools.
+- Analyzing code quality by comparing code with its corresponding unit tests.
+- Training and testing LLM models for automated unit test generation.
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+The motivation for creating this dataset is to provide a comprehensive collection of code and unit tests from various reputable open-source projects. This can facilitate research and development in the areas of automated testing, code quality analysis, and LLM for software engineering.
+
+### Source Data
+
+#### Data Collection and Processing
+
+The data was collected from public GitHub repositories. The selection criteria included repositories with well-documented code and corresponding unit tests. The data was filtered and normalized to ensure consistency.
+
+#### Who are the source data producers?
+
+The source data producers are the contributors to the respective open-source GitHub repositories.
+
+## Bias, Risks, and Limitations
+
+The dataset may have biases based on the coding practices and testing methodologies of the included repositories. It may not cover all possible scenarios and edge cases in software testing.
+
+## Citation [optional]
+
+