Update README.md

README.md

@@ -55,16 +55,15 @@ configs:
     path: kaggle/train-*
 ---
 
-# 📝 GitHub Issues & Notebooks
+# GitHub Issues & Kaggle 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.  
+GitHub Issues & Kaggle Notebooks is a collection of two code datasets intended for language models training, they are sourced from GitHub issues and notebooks in Kaggle platform. These datasets are a modified part of the [StarCoder2](https://arxiv.org/abs/2402.19173) model training corpus, precisely the [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:
+The dataset includes:
 
-- 🐛 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)
+- 🐛 GitHub Issues – 11B tokens of discussions from GitHub issues sourced from [GH Archive](https://www.gharchive.org/).
+- 📊 Kaggle Notebooks – 1.7B tokens of data analysis notebooks in markdonw format, curated from Kaggle's [Meta Kaggle Code](https://www.kaggle.com/datasets/kaggle/meta-kaggle-code) dataset.
+These datasets have undergone filtering to remove low-quality content, duplicates and PII. More details in StarCoder2 [paper](https://arxiv.org/abs/2402.19173)
 
 ## How to load the dataset
 
@@ -73,13 +72,12 @@ 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
+issues = load_dataset("HuggingFaceTB/github-issues-notebooks", "issues", split="train")  # GitHub Issues
+kaggle_notebooks = load_dataset("HuggingFaceTB/github-issues-notebooks", "kaggle", split="train")  # Kaggle 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
+These curation details are from the SterCoder2 pipeline. The original datasets can be found at: https://huggingface.co/datasets/bigcode/starcoder2data-extras and more details can be found in the StarCoder2 paper.
 
 ### 🐛 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.
@@ -89,20 +87,92 @@ 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.
+- Anonymizing usernames while preserving the conversation structure, names, emails, keys, passwords, IP addresses using [StarPII](https://huggingface.co/bigcode/starpii).
 
-## 💻 Jupyter Notebooks
-The Jupyter Notebooks dataset consists of 4M deduplicatedstructured notebooks, converted to Python scripts using Jupytext.
+We format the conversatiosn using this template:
+
+```
+Title: [Issue title]
+
+Question:
+username_0: [Issue content]
+
+Answers:
+username_1: [Answer from user 1]
+username_0: [Author reply]
+username_2: [Answer from user 2]
+...
+Status: Issue closed (optional)
+```
 
 ## 📊 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%
+- Extracting metadata for notebooks that reference Kaggle datasets. When possible, we retrieve the datasets references in the notebook and add information about them to the beginning of the notebook (description, `ds.info()` output and 4 examples)
+- Filtering out duplicates, which reduced the dataset volume by 78%, and redacting PII.
+Each notebook is formatted in Markdown format, where we start with the notebook title, dataset description when available and put the notebook (converted to a Python script) in a code block. 
+
+Below is an example of a kaggle notebook:
+
+````
+# Iris Flower Dataset
+
+### Context
+The Iris flower data set is a multivariate data set introduced ... (truncated)
+
+```python
+import pandas as pd
 
+df = pd.read_csv('iris-flower-dataset/IRIS.csv')
+df.info()
+```
+```
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 150 entries, 0 to 149
+Data columns (total 5 columns):
+ #   Column        Non-Null Count  Dtype  
+---  ------        --------------  -----  
+ 0   sepal_length  150 non-null    float64
+ 1   sepal_width   150 non-null    float64
+ 2   petal_length  150 non-null    float64
+ 3   petal_width   150 non-null    float64
+ 4   species       150 non-null    object 
+dtypes: float64(4), object(1)
+memory usage: 6.0+ KB
+```
+
+Examples from the dataset:
+```
+{
+    "sepal_length": 5.1,
+    "sepal_width": 3.5,
+    "petal_length": 1.4,
+    "petal_width": 0.2,
+    "species": "Iris-setosa"
+}
+... (truncated)
+```
+
+Code:
+```python
+import numpy as np  # linear algebra
+import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+import os
+
+for dirname, _, filenames in os.walk("/kaggle/input"):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+import matplotlib.pyplot as plt
+
+data = pd.read_csv("/kaggle/input/iris-flower-dataset/IRIS.csv")
+data.head()
+X = data.drop("species", axis=1)
+... (truncated)
+````
 
 ## Citation
 ```