ragas
Datasets
All datasets matching “ragas”ragas-wikiqa
Dataset Card for "ragas-wikiqa"
More Information needed
ragas-golden-dataset
Dataset Card for the ragas-golden-dataset
Dataset Description
The RAGAS Golden Dataset is a synthetically generated question-answering dataset designed for evaluating Retrieval Augmented Generation (RAG) systems. It contains high-quality question-answer pairs derived from academic papers on AI agents and agentic AI architectures.
Dataset Summary
This dataset was generated using Prefect and the RAGAS TestsetGenerator framework, which creates synthetic questions… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/ragas-golden-dataset.EnDev_RAGAS_testset
EnDev RAGAS Test Set
Synthetic Q&A test set (42 pairs) generated with RAGAS
(TestsetGenerator.generate_with_chunks) over chunks sampled from the EnDev corpus
stored in Qdrant collection endev-bgem3-512 (Gradio-gateway Space
GIZ/EnDev_Qdrant).
Generated: 2026-09-14 UTC
Generator/judge LLM: Qwen/Qwen3-235B-A22B-Instruct-2507
Embeddings: BGE-M3 via the EnDev TEI Inference Endpoint
Columns: user_input, reference, reference_contexts, synthesizer_name
Used to evaluate the deployed… See the full description on the dataset page: https://huggingface.co/datasets/GIZ/EnDev_RAGAS_testset.ragas-golden-dataset-documents
Dataset Card for RAGAS Golden Dataset Documents
A small, mixed‐format corpus to compare PDF, API, and web‐based document loader output from the LangChain ecosystem.
The code to run the Prefect prefect_docloader_pipeline.py pipeline is available in the RAGAS Golden Dataset Pipeline repository.
While several enhancements are planned for future iterations, the hands-on insights gained from this grassroots exploration of document loader behaviors proved too valuable -- things that… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/ragas-golden-dataset-documents.RAGAS_xquad_x_squadtest_half split from XQuAD https://huggingface.co/datasets/xxizhouu/RAGAS_xquad
PLUS
one impossible question(english) for each paragraph, taken from SQuAD 2.0
test_id: shared uuid accross different spilt
cmi: code mix index
rag_assets_10292024_filtered_v1
