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# 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?"
}
]
}