--- title: FlashRAG_datasets canonical_url: "https://www.modelscope.cn/datasets/yamseyoung/FlashRAG_datasets" md_url: "https://www.modelscope.cn/datasets/yamseyoung/FlashRAG_datasets.md" repository: yamseyoung/FlashRAG_datasets last_updated: 2025-07-10 license: cc-by-sa-4.0 storage_size: "30 GB" downloads: 223 stars: 0 --- # FlashRAG_datasets > FlashRAG_datasets - yamseyoung 在 ModelScope 开源的数据集。⚡FlashRAG: A Python Toolkit for Efficient RAG Research yamseyoung/FlashRAG_datasets 是 ModelScope 魔搭社区上的数据集，存储大小 30 GB，采用 cc-by-sa-4.0 许可。 - **Repository**: yamseyoung/FlashRAG_datasets - **License**: cc-by-sa-4.0 - **Storage size**: 30 GB - **Downloads**: 223 - **Stars**: 0 - **Last updated**: 2025-07-10 Source: https://www.modelscope.cn/datasets/yamseyoung/FlashRAG_datasets --- # ⚡FlashRAG: A Python Toolkit for Efficient RAG Research FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms. With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components. For more information, please view our GitHub repo and paper: GitHub repo: [https://github.com/RUC-NLPIR/FlashRAG/](https://github.com/RUC-NLPIR/FlashRAG/) Paper link: [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://arxiv.org/abs/2405.13576). # Dataset Card for FlashRAG Datasets We have collected and processed 35 datasets widely used in RAG research, pre-processing them to ensure a consistent format for ease of use. For certain datasets (such as Wiki-asp), we have adapted them to fit the requirements of RAG tasks according to the methods commonly used within the community. ## Dataset Details For each dataset, we save each split as a `jsonl` file, and each line is a dict as follows: ```python { 'id': str, 'question': str, 'golden_answers': List[str], 'metadata': dict } ``` Below is the list of datasets along with the corresponding sample sizes: | Task | Dataset Name | Knowledge Source | # Train | # Dev | # Test | |---------------------------|-----------------|------------------|-----------|---------|--------| | QA | NQ | wiki | 79,168 | 8,757 | 3,610 | | QA | TriviaQA | wiki & web | 78,785 | 8,837 | 11,313 | | QA | PopQA | wiki | / | / | 14,267 | | QA | SQuAD | wiki | 87,599 | 10,570 | / | | QA | MSMARCO-QA | web | 808,731 | 101,093 | / | | QA | NarrativeQA | books and story | 32,747 | 3,461 | 10,557 | | QA | WikiQA | wiki | 20,360 | 2,733 | 6,165 | | QA | WebQuestions | Google Freebase | 3,778 | / | 2,032 | | QA | AmbigQA | wiki | 10,036 | 2,002 | / | | QA | SIQA | - | 33,410 | 1,954 | / | | QA | CommonSenseQA | - | 9,741 | 1,221 | / | | QA | BoolQ | wiki | 9,427 | 3,270 | / | | QA | PIQA | - | 16,113 | 1,838 | / | | QA | Fermi | wiki | 8,000 | 1,000 | 1,000 | | multi-hop QA | HotpotQA | wiki | 90,447 | 7,405 | / | | multi-hop QA | 2WikiMultiHopQA | wiki | 15,000 | 12,576 | / | | multi-hop QA | Musique | wiki | 19,938 | 2,417 | / | | multi-hop QA | Bamboogle | wiki | / | / | 125 | | multi-hop QA | StrategyQA | wiki | 2290 | / | / | | Long-form QA | ASQA | wiki | 4,353 | 948 | / | | Long-form QA | ELI5 | Reddit | 272,634 | 1,507 | / | | Long-form QA | WikiPassageQA | wiki | 3,332 | 417 | 416 | | Open-Domain Summarization | WikiASP | wiki | 300,636 | 37,046 | 37,368 | | multiple-choice | MMLU | - | 99,842 | 1,531 | 14,042 | | multiple-choice | TruthfulQA | wiki | / | 817 | / | | multiple-choice | HellaSWAG | ActivityNet | 39,905 | 10,042 | / | | multiple-choice | ARC | - | 3,370 | 869 | 3,548 | | multiple-choice | OpenBookQA | - | 4,957 | 500 | 500 | | multiple-choice | QuaRTz | - | 2696 | 384 | 784 | | Fact Verification | FEVER | wiki | 104,966 | 10,444 | / | | Dialog Generation | WOW | wiki | 63,734 | 3,054 | / | | Entity Linking | AIDA CoNll-yago | Freebase & wiki | 18,395 | 4,784 | / | | Entity Linking | WNED | Wiki | / | 8,995 | / | | Slot Filling | T-REx | DBPedia | 2,284,168 | 5,000 | / | | Slot Filling | Zero-shot RE | wiki | 147,909 | 3,724 | / | | In-domain QA| DomainRAG | Web pages of RUC| / | / | 485| ## Retrieval Corpus We also provide a corpus document library for retrieval, with the path in FlashRAG/retrieval-corpus. ```jsonl {"id":"0", "contents": "...."} {"id":"1", "contents": "..."} ``` The `contents` key is essential for building the index. For documents that include both text and title, we recommend setting the value of `contents` to `{title}\n{text}`. The corpus file can also contain other keys to record additional characteristics of the documents. Detail information of provided can be found in our github link: [https://github.com/RUC-NLPIR/FlashRAG?tab=readme-ov-file#document-corpus](https://github.com/RUC-NLPIR/FlashRAG?tab=readme-ov-file#document-corpus). ## Citation **BibTeX:** Please kindly cite our paper if helps your research: ```BibTex @article{FlashRAG, author={Jiajie Jin and Yutao Zhu and Xinyu Yang and Chenghao Zhang and Zhicheng Dou}, title={FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research}, journal={CoRR}, volume={abs/2405.13576}, year={2024}, url={https://arxiv.org/abs/2405.13576}, eprinttype={arXiv}, eprint={2405.13576} } ```