Datasets:
File size: 4,156 Bytes
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language:
- en
license: other
license_name: github-terms-of-service
task_categories:
- question-answering
- information-retrieval
tags:
- rag
- github-issues
- cross-issue-reasoning
- silver-labels
pretty_name: MetaRAG Cross-Issue OSSQA
size_categories:
- 10K<n<100K
configs:
- config_name: default
default: true
data_files:
- split: dev
path: data/default/dev.jsonl
- split: test
path: data/default/test.jsonl
- config_name: queries
data_files:
- split: dev
path: data/queries/dev.jsonl
- split: test
path: data/queries/test.jsonl
- config_name: corpus
data_files:
- split: corpus
path: data/corpus/corpus.jsonl
---
# MetaRAG Cross-Issue OSSQA
Dataset page: <https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA>
MetaRAG Cross-Issue OSSQA is an English open-source software issue question-answering and retrieval benchmark. Each example asks a question grounded in one GitHub issue and requires evidence from a related issue. The data contains explicit cross-issue references and a three-document silver evidence path.
## Dataset configurations
| Configuration | Splits | Rows | Purpose |
| --- | --- | ---: | --- |
| `default` | `dev`, `test` | 100 / 400 | Complete evaluation records, including embedded issue fragments and silver labels. |
| `queries` | `dev`, `test` | 100 / 400 | Retrieval and QA queries with answer and evidence-path supervision. |
| `corpus` | `corpus` | 9,536 | Deduplicated issue-description and issue-comment fragments for retrieval. This is a knowledge corpus, not a training split. |
## Loading
```python
from datasets import load_dataset
evaluation = load_dataset("MapleBi/MetaRAG_Cross-Issue_OSSQA")
queries = load_dataset("MapleBi/MetaRAG_Cross-Issue_OSSQA", "queries")
corpus = load_dataset("MapleBi/MetaRAG_Cross-Issue_OSSQA", "corpus", split="corpus")
```
## Main fields
`default` includes the query, its known context, answer, source and target issue metadata, `silver_evidence_path`, quality signals, and the ordered `fragments` supporting each example. `queries` is the compact query-level representation. `corpus` contains one searchable fragment per `document_id`, plus issue, repository, author, timestamp, and cross-issue-link metadata.
The fields `answer_document_id` and `silver_evidence_path` refer to `corpus.document_id`. `known_context_document_ids` identifies documents supplied as query context; these are excluded from the gold evidence path. `split` is repository-disjoint between `dev` and `test`.
## Data creation
The dataset was constructed from public GitHub event data via OpenDigger / GH Archive, from 2022-01-01 through 2025-12-31. It contains 500 qualified cross-issue samples from 468 repositories. The sampling procedure requires an explicit cross-issue reference, a path spanning at least two distinct issues, English-language filtering, deterministic diversity sampling, and repository-disjoint development/test partitions.
See `dataset_statistics.json` for the complete sampling settings, quality distribution, year distribution, and leakage audit result.
## Labels and limitations
`reference_answer` and `silver_evidence_path` are heuristic silver annotations. They should not be treated as human-validated gold labels without additional review. Source text may contain stale links, personal identifiers visible in public GitHub content, inaccurate technical claims, or other artifacts of public issue discussions.
The data is intended for research and evaluation. Users are responsible for ensuring their use complies with the applicable [GitHub Terms of Service](https://docs.github.com/en/site-policy/github-terms/github-terms-of-service), the source platforms' policies, and their own privacy, legal, and ethical requirements.
## Provenance
- OpenDigger: <https://github.com/X-lab2017/open-digger>
- GH Archive: <https://www.gharchive.org/>
## Repository integrity
`MANIFEST.sha256` contains SHA-256 checksums for every data file and `dataset_statistics.json` in this package. To regenerate the package from the accompanying source files, remove this output directory and run `python3 prepare_hf_dataset.py`.
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