# GitHub Issues Dataset Card

**Author / Maintainer:** @xanderIV  
**Point of Contact:** @xanderIV  

---

## Dataset Description

### Dataset Summary

GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository:  
https://github.com/huggingface/datasets  

This dataset is curated and documented by **@xanderIV** for educational and research purposes.  

It can be used for:

- Semantic search  
- Multilabel text classification  
- Automated issue triaging  
- Topic modeling  
- LLM fine-tuning  
- Retrieval-Augmented Generation (RAG) experiments  

The dataset contains English-language technical discussions related to NLP, computer vision, speech, multimodal ML systems, and ML infrastructure.

This dataset is particularly relevant for:

- Developer assistant systems  
- LLM-powered support automation  
- DevOps / MLOps / LLMOps workflows  
- Research in applied ML systems  

---

## Supported Tasks and Leaderboards

### 1. Text Classification (`text-classification`)

The dataset can be used for **multilabel text classification**, where a model predicts one or more labels (e.g., `bug`, `enhancement`, `documentation`) for each issue.

**Typical metrics:**
- F1 score  
- Accuracy  
- Precision  
- Recall  

**Suggested models:**
- `distilbert-base-uncased`
- `roberta-base`
- `microsoft/deberta-v3-base`

---

### 2. Information Retrieval (`information-retrieval`)

The dataset can be used for **semantic search**, where the task is to retrieve the most relevant GitHub issue given a user query.

**Typical metrics:**
- MRR (Mean Reciprocal Rank)  
- Recall@k  

**Suggested models:**
- `sentence-transformers/all-MiniLM-L6-v2`
- `BAAI/bge-base-en-v1.5`
- `intfloat/e5-base`

---

### 3. Issue Triaging (`other:issue-triaging`)

The dataset can be used for automated issue routing and classification.

The task consists of:
- Predicting labels  
- Suggesting maintainers  
- Routing issues to appropriate teams  

**Metrics:**
- Classification accuracy  
- Routing precision  

---

### 4. LLM Fine-Tuning (`other:llm-finetuning`)

The dataset can be used to fine-tune large language models for:

- Developer assistants  
- Issue summarization  
- Pull request review generation  
- Support automation  

**Suggested models:**
- `meta-llama/Llama-3-8B-Instruct`
- `mistralai/Mistral-7B-Instruct-v0.2`
- `Qwen/Qwen2.5-7B-Instruct`

---

## Languages

- Primary language: English (`en`, BCP-47)

Text characteristics:
- Technical discussions  
- Developer communication  
- Bug reports  
- Code snippets (Python, YAML, JSON, etc.)  
- Configuration files  

Language style: semi-formal, domain-specific (software engineering / ML infrastructure).

---

## Dataset Structure

### Data Instances

Example instance:

```json
{
  "issue_id": 12345,
  "title": "Dataset loading fails with streaming=True",
  "body": "When trying to load the dataset with streaming enabled...",
  "labels": ["bug", "datasets"],
  "author": "username",
  "created_at": "2023-06-10T14:32:00Z",
  "comments": [
    {
      "author": "maintainer",
      "text": "Can you provide the stack trace?"
    }
  ]
}