Datasets:

BeNatural
/

github-scikit-learn-issues-dataset

+---
+pretty_name: GitHub Scikit-learn Issues Dataset
+language:
+- en
+license: other
+size_categories:
+- n<1K
+task_categories:
+- text-classification
+- sentence-similarity
+tags:
+- github
+- scikit-learn
+- issue-tracking
+- software-engineering
+- open-data
+- text
+---
+# GitHub Scikit-learn Issues Dataset
+## 1. Overview
+This dataset contains 300 issues collected from the public GitHub repository
+[scikit-learn/scikit-learn](https://github.com/scikit-learn/scikit-learn). It was created for an
+AI/data assessment (Weeks 9-12) to demonstrate dataset creation, a Transformers pipeline and
+FAISS semantic search, with documentation of sourcing, limitations and compliance.
+- Dataset: https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset
+- Source: https://github.com/scikit-learn/scikit-learn/issues
+- Format: Parquet, one `train` split, 300 rows, 14 columns
+## 2. Why this dataset
+GitHub issues are real, human-written technical text (bug reports, feature requests, documentation
+problems) paired with structured metadata (labels, dates, state, comment counts). This makes them
+suitable for text classification, sentiment/zero-shot analysis and semantic search for similar
+issues, and the scikit-learn project is well known, openly developed and well documented.
+## 3. Data collection
+- Platform: GitHub (public issues of `scikit-learn/scikit-learn`)
+- Collection method: issues were retrieved from the GitHub REST API (`https://api.github.com/repos/scikit-learn/scikit-learn/issues`) using Python and the `requests` library, then assembled into a pandas DataFrame, saved as CSV and published to the Hugging Face Hub in Parquet format.
+- Collection date: 12 October 2026
+- Authentication: requests were authenticated with a GitHub personal access token. The token is not included in this dataset or repository.
+- Scope: the dataset contains issues only. No pull requests are included (no record has a `/pull/` URL).
+- Selection: the 300 records are the **oldest** issues in the repository (issue numbers 1 to 768, created between 2010-08-31 and 2012-04-13). They are not a random sample.
+## 4. Dataset structure
+| Column | Type | Description |
+|---|---|---|
+| `issue_id` | int | GitHub's global issue ID |
+| `issue_number` | int | Issue number within the repository |
+| `repository` | string | `scikit-learn/scikit-learn` |
+| `title` | string | Issue title |
+| `body` | string | Issue description (9 records are empty) |
+| `state` | string | `closed` (295) or `open` (5) |
+| `labels` | string | Comma-separated GitHub labels (empty for 235 records) |
+| `created_at` | datetime | When the issue was opened (UTC) |
+| `closed_at` | datetime | When the issue was closed (empty for open issues) |
+| `comments` | int | Number of comments on the issue (count only; comment text is not included) |
+| `author` | string | Public GitHub username of the issue author |
+| `html_url` | string | Link to the original issue |
+| `text_length` | int | Derived field: length of the issue text (see Section 5) |
+| `time_to_close_hours` | float | Derived field: hours between `created_at` and `closed_at` (empty for open issues) |
+## 5. Processing and augmentation
+- Raw issue records were assembled into a structured table (one row per issue).
+- Pull requests were excluded so that only issues remain.
+- Duplicates: none found (all `issue_id`, `issue_number` and `html_url` values are unique).
+- Derived fields added: `text_length` and `time_to_close_hours`.
+- Missing values were **kept, not imputed**: 9 empty bodies, 235 records without labels, and 5 open issues without `closed_at` / `time_to_close_hours`.
+## 6. Initial data summary
+| Item | Value |
+|---|---|
+| Rows / columns | 300 / 14 |
+| Date range | 2010-08-31 to 2012-04-13 |
+| State | 295 closed, 5 open |
+| Issues with labels | 65 of 300 (22%) |
+| Most common labels | New Feature (21), Enhancement (11), Bug (7) |
+| Comments per issue | median 4, mean 7.2, max 59 |
+| Time to close | median about 1,195 hours (about 50 days); highly skewed (max about 125,700 hours) |
+| Distinct authors | 84; the top 3 (amueller, fabianp, mblondel) wrote 132 of the 300 issues |
+## 7. Intended uses
+1. **Transformers pipeline:** apply a pretrained model (e.g. sentiment or zero-shot classification) to titles/bodies and examine the outputs.
+2. **Semantic search:** embed issue text and build a FAISS index to retrieve similar issues for natural-language queries.
+Not intended for: profiling individual contributors, or drawing conclusions about the modern scikit-learn project.
+## 8. Limitations and bias
+- **Temporal bias:** all issues are from 2010-2012, the earliest days of the project. Topics, labels and writing style differ from current issues, and many early issues were written by a handful of core developers.
+- **Small sample:** 300 rows, not representative of the repository's full history.
+- **Author concentration:** a few authors account for a large share of records.
+- **Sparse labels:** 78% of issues have no label, so label-based analysis covers a minority.
+- **Short or empty text:** some bodies are empty or very short, which weakens classification and embedding quality.
+- **Open vs closed:** only 5 issues are open, so resolution-time analysis is dominated by closed issues.
+- **Model limitations:** general-purpose sentiment models often misread technical text; semantic search quality depends on the embedding model.
+- **No comment text:** only the number of comments is included.
+## 9. Ethics, legal and compliance
+- **Privacy:** issues are public, but they include public GitHub usernames and can contain links or occasional email-like text. Usernames have been kept as they appear publicly on GitHub. The dataset is not used to identify or profile individuals.
+- **Attribution:** content originates from the scikit-learn GitHub project and its contributors; every record links back to its original issue via `html_url`.
+- **Licensing:** the scikit-learn software is BSD-3-Clause. This licence is for the software repository and is not assumed to cover user-written issue text, so this dataset is marked `license: other` and is shared for educational and research purposes only. Users should check GitHub's Terms of Service and the original project's terms before reuse.
+- **Terms of service:** data was collected from publicly available content through the official GitHub API.
+- **Organisational policy alignment:** the dataset was collected, stored and documented in line with the organisation's data storage, management and compliance policies provided for this assessment: public source data only, no private data, attribution to the original source, secure handling of credentials, and versioned storage.
+- **Security:** API tokens are not stored in the notebooks or this repository.
+## 10. Risks and corrective actions
+| Risk | Mitigation |
+|---|---|
+| Personal data in public issue text | Keep only public handles; document handling; remove or anonymise `author` if required; do not target individuals |
+| Temporal / sampling bias | Document the 2010-2012 window; in future, collect a random or time-stratified sample |
+| Licence ambiguity of user content | Mark as `other`, link back to originals, educational use only |
+| Missing labels / bodies | Keep missing values visible, report counts, avoid over-interpreting |
+| Misleading model output | Manually review sample predictions and state model limits |
+| Data loss | Dataset on the Hugging Face Hub with version history; notebooks and outputs backed up per policy |
+## 11. Storage, versioning and reproducibility
+- Published on the Hugging Face Hub (git-based, so every commit is versioned): https://huggingface.co/datasets/BeNatural/github-scikit-learn-issues-dataset
+- Notebooks: three notebooks (data collection and augmentation, Transformers pipeline, FAISS semantic search) are submitted with this assessment as evidence.
+- Local copy: the CSV and notebooks are kept as a backup copy alongside the project files.
+- Version: v1.0, collected 12 October 2026. Any later changes to the data or processing should be recorded here.
+## 12. References
+- scikit-learn repository: https://github.com/scikit-learn/scikit-learn
+- scikit-learn issues: https://github.com/scikit-learn/scikit-learn/issues
+- GitHub REST API (issues): https://docs.github.com/en/rest/issues
+- Hugging Face LLM Course, Creating your own dataset: https://huggingface.co/learn/llm-course/chapter5/5?fw=pt
+- Hugging Face LLM Course, Behind the pipeline: https://huggingface.co/learn/llm-course/chapter2/2?fw=pt
+- Hugging Face LLM Course, Semantic search with FAISS: https://huggingface.co/learn/llm-course/chapter5/6?fw=pt
+- Datasheets for Datasets (Gebru et al.): https://arxiv.org/abs/1803.09010