ECSS-1.0 Dataset

Dataset Summary

This dataset provides a focused benchmark for retrieval and generation tasks related to ECSS (European Cooperation for Space Standardization) documents. It includes a set of documents, queries, relevance judgments (qrels), and page images.

• Number of Documents: 196
• Number of Queries: 67
• Number of Pages: 22700
• Number of Relevance Judgments (qrels): 89
• Average Number of Pages per Query: 1.3

Dataset Structure (Hugging Face Datasets)

The dataset is structured into the following subsets:

• corpus: Contains page-level information:
  • _id: A unique identifier for this specific page within the corpus.
  • title: The title of the document.
  • text: The text of the document.
• queries: Contains query information:
  • _id: Unique identifier for the question.
  • query_text: The question text.
  • relevant_document_ids: A list of corpus documents considered as references for this question, each reference containing:
    • corpus_id: The document identifier.
    • score: The importance or relevance score.

Usage Examples

You can load the datasets using the load_from_disk function from the datasets library. Replace the paths with the actual locations on your machine.

from datasets import load_dataset
dataset_queries_test = load_dataset("FOR-sight-ai/ECSS-1.0", "queries", split="test")

Results

Model Name	nDCG@10
bm25	0.43
bge-large-en-v1.5	0.44
nomic-embed-multimodal-3b	0.59
colqwen2.5-v0.2	0.68

Citation

If you use this dataset in your research or work, please cite:

@misc{ecssbenchmark2025,
  title={ECSS RAG benchmark},
  author={Francois Lancelot and Nawal Ould Amer and Benjamin Fourreau and Catherine Kobus and Marion-Cécile Martin},
  primaryClass={cs.IR},
  year={2025},
}