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Datasets:
MapleBi
/
MetaRAG_Cross-Issue_OSSQA

Tasks:
Question Answering
Modalities:
Tabular
Text
Formats:
json
Languages:
English
Size:
10K - 100K
Tags:
rag
github-issues
cross-issue-reasoning
silver-labels
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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  2. README.md +5 -3
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  # MetaRAG Cross-Issue OSSQA
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  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.
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  ## Dataset configurations
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  ```python
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  from datasets import load_dataset
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- evaluation = load_dataset("<namespace>/metarag-cross-issue-ossqa")
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- queries = load_dataset("<namespace>/metarag-cross-issue-ossqa", "queries")
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- corpus = load_dataset("<namespace>/metarag-cross-issue-ossqa", "corpus", split="corpus")
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  ```
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  ## Main fields
 
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  # MetaRAG Cross-Issue OSSQA
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+ Dataset page: <https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA>
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+
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  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.
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  ## Dataset configurations
 
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  ```python
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  from datasets import load_dataset
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+ evaluation = load_dataset("MapleBi/MetaRAG_Cross-Issue_OSSQA")
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+ queries = load_dataset("MapleBi/MetaRAG_Cross-Issue_OSSQA", "queries")
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+ corpus = load_dataset("MapleBi/MetaRAG_Cross-Issue_OSSQA", "corpus", split="corpus")
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  ```
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  ## Main fields