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README.md

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     path: queries/test-*
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+# Vidore Benchmark 2 - ESG Human Labeled
+
+This dataset is part of the "Vidore Benchmark 2" collection, designed for evaluating visual retrieval applications. It focuses on the theme of **ESG reports from the fast food industry**.
+
+## Dataset Summary
+
+Each query is in english.
+
+This dataset provides a focused benchmark for visual retrieval tasks related to ESG reports for the fast food industry. It includes a curated set of documents, queries, relevance judgments (qrels), and page images.
+This dataset was fully labelled by hand, has no overlap of queries with its synthetic counterpart (available [here](https://huggingface.co/datasets/vidore/synthetic_rse_restaurant_filtered_v1.0))
+
+*   **Number of Documents:** 27
+*   **Number of Queries:** 52
+*   **Number of Pages:** 1538
+*   **Number of Relevance Judgments (qrels):** 128
+*   **Average Number of Pages per Query:** 2.5
+
+## Dataset Structure (Hugging Face Datasets)
+The dataset is structured into the following columns:
+
+*   **`corpus`**: Contains page-level information:
+    *   `"image"`:  The image of the page (a PIL Image object).
+    *   `"corpus-id"`: A unique identifier for this specific page within the corpus.
+*   **`queries`**: Contains query information:
+    *   `"query-id"`: A unique identifier for the query.
+    *   `"query"`: The text of the query.
+*   **`qrels`**: Contains relevance judgments:
+    *   `"corpus-id"`: The ID of the relevant page.
+    *   `"query-id"`: The ID of the query.
+    *   `"answer"`: Answer relevant to the query AND the page.
+    *   `"score"`: The relevance score.
+
+
+## Usage
+
+This dataset is designed for evaluating the performance of visual retrieval systems, particularly those focused on document image understanding.
+
+**Example Evaluation with ColPali (CLI):**
+
+Here's a code snippet demonstrating how to evaluate the ColPali model on this dataset using the `vidore-benchmark` command-line tool.
+
+1.  **Install the `vidore-benchmark` package:**
+
+    ```bash
+    pip install vidore-benchmark datasets
+    ```
+
+2.  **Run the evaluation:**
+
+    ```bash
+    vidore-benchmark evaluate-retriever \
+        --model-class colpali \
+        --model-name vidore/colpali-v1.3 \
+        --dataset-name vidore/restaurant_esg_reports_beir \
+        --dataset-format beir \
+        --split test
+    ```
+    
+For more details on using `vidore-benchmark`, refer to the official documentation: [https://github.com/illuin-tech/vidore-benchmark](https://github.com/illuin-tech/vidore-benchmark)
+
+## Citation
+
+If you use this dataset in your research or work, please cite:
+
+#INSERT CITATION
+
+## License
+
+#INSERT LICENSE
+
+## Acknowledgments
+
+This work is partially supported by [ILLUIN Technology](https://www.illuin.tech/), and by a grant from ANRT France.