---
dataset_info:
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    dtype: int64
  - name: image
    dtype: image
  - name: pdf_url
    dtype: string
  - name: company
    dtype: string
  - name: date
    dtype: string
  splits:
  - name: test
    num_bytes: 842829685.81
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- config_name: qrels
  features:
  - name: query-id
    dtype: int64
  - name: corpus-id
    dtype: int64
  - name: score
    dtype: int64
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    num_examples: 128
  download_size: 2521
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- config_name: queries
  features:
  - name: query-id
    dtype: int64
  - name: query
    dtype: string
  - name: source_type
    sequence: string
  - name: answer
    dtype: string
  splits:
  - name: test
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    num_examples: 52
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configs:
- config_name: corpus
  data_files:
  - split: test
    path: corpus/test-*
- config_name: qrels
  data_files:
  - split: test
    path: qrels/test-*
- config_name: queries
  data_files:
  - split: test
    path: queries/test-*
---

# 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:

```bibtex
@misc{faysse2024colpaliefficientdocumentretrieval,
  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
  author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
  year={2024},
  eprint={2407.01449},
  archivePrefix={arXiv},
  primaryClass={cs.IR},
  url={https://arxiv.org/abs/2407.01449}, 
}
```

## Acknowledgments

This work is partially supported by [ILLUIN Technology](https://www.illuin.tech/), and by a grant from ANRT France.