Update README.md

README.md

@@ -54,3 +54,62 @@ configs:
   - split: train
     path: kaggle/train-*
 ---
+
+# 📝 GitHub Issues & Notebooks
+## Description
+📝 GitHub Issues & Notebooks is a collection of code datasets intended for language models training, they are sourced from GitHub issues, Kaggle notebooks, and Jupyter notebooks. These datasets are part of the [StarCoder2](https://arxiv.org/abs/2402.19173) model training corpus and a modified subset of [bigcode/StarCoder2-Extras](https://huggingface.co/datasets/bigcode/starcoder2data-extras) dataset. We reformat the samples to remove StarCoder2's special tokens and use natural text to delimit comments in issues and display kaggle notebooks in markdown and code blocks.  
+
+The dataset includes three subsets:
+
+- 🐛 GitHub Issues – 11B tokens of technical discussions and issue tracking from GitHub repositories.
+- 📊 Kaggle Notebooks – 2B tokens of data analysis notebooks curated from Kaggle.
+- 💻 Jupyter Notebooks – 16B tokens of Jupyter notebooks converted to Python scripts for better processing.
+These subsets have undergone filtering to remove low-quality content, duplicates, more details in StarCoder2 [paper](https://arxiv.org/abs/2402.19173)
+
+## How to load the dataset
+
+You can load a specific subset using the following code:
+
+```python
+from datasets import load_dataset
+
+data = load_dataset("HuggingFaceTB/github-issues-notebooks", "issues", split="train")  # GitHub Issues
+data = load_dataset("HuggingFaceTB/github-issues-notebooks", "kaggle", split="train")  # Kaggle Notebooks
+data = load_dataset("HuggingFaceTB/github-issues-notebooks", "jupyter", split="train")  # Jupyter Notebooks
+```
+
+## Dataset curation
+These curation details are from the SterCoder2 pipeline. The original datasets can be found at: https://huggingface.co/datasets/bigcode/starcoder2data-extras
+
+### 🐛 GitHub Issues
+The GitHub Issues dataset consists of discussions from GitHub repositories, sourced from GHArchive. It contains issue reports, bug tracking, and technical Q&A discussions.
+
+To ensure high-quality data, the StarCoder2 processing pipeline included:
+
+- Removing bot-generated comments and auto-replies from email responses.
+- Filtering out short issues (<200 characters) and extremely long comments.
+- Keeping only discussions with multiple users (or highly detailed single-user reports).
+- Anonymizing usernames while preserving the conversation structure.
+- This cleaning process removed 38% of issues, ensuring a high-quality dataset with technical depth.
+- More details can be found in the StarCoder2 paper.
+
+## 💻 Jupyter Notebooks
+The Jupyter Notebooks dataset consists of 4M deduplicatedstructured notebooks, converted to Python scripts using Jupytext.
+
+## 📊 Kaggle Notebooks
+The Kaggle Notebooks are sourced from the [Meta Kaggle Code](https://www.kaggle.com/datasets/kaggle/meta-kaggle-code) dataset. They were cleaned using a multi-step filtering process, which included:
+
+- Removing notebooks with syntax errors or less than 100 characters.
+- Extracting metadata for notebooks that reference Kaggle datasets, when possible we retrieve the datasets and append information about the data to the beginning of the notebooks (description, `ds.info()`output and 4 examples)
+- Filtering out duplicates, which reduced the dataset volume by 78%
+
+
+## Citation
+```
+@article{lozhkov2024starcoder,
+  title={Starcoder 2 and the stack v2: The next generation},
+  author={Lozhkov, Anton and Li, Raymond and Allal, Loubna Ben and Cassano, Federico and Lamy-Poirier, Joel and Tazi, Nouamane and Tang, Ao and Pykhtar, Dmytro and Liu, Jiawei and Wei, Yuxiang and others},
+  journal={arXiv preprint arXiv:2402.19173},
+  year={2024}
+}
+```