Update README.md

README.md

@@ -82,3 +82,52 @@ configs:
   - split: test
     path: queries/test-*
 ---
+
+# 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: 42
+- Number of Pages: 22700
+- Number of Relevance Judgments (qrels): 58
+- Average Number of Pages per Query: 1.4
+
+## 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.
+
+```python
+from datasets import load_dataset
+dataset_queries_test = load_dataset("FOR-sight-ai/ECSS-1.0", "queries", split="test")
+```
+
+## Citation
+
+If you use this dataset in your research or work, please cite:
+
+```bibtex
+@misc{ecssbenchmark2025,
+  title={ECSS benchmark},
+  author={Francois Lancelot and Nawal Ould Amer and Benjamin Fourreau and Catherine Kobus and Marion-Cécile Martin},
+  primaryClass={cs.IR},
+  year={2025},
+}
+```