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    path: documents/test-*
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  data_files:
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---

# ConTEB - ESG Reports

This dataset is part of *ConTEB* (Context-aware Text Embedding Benchmark), designed for evaluating contextual embedding model capabilities. It focuses on the theme of **Industrial ESG Reports**, particularly stemming from the fast-food industry.

## Dataset Summary

This dataset was designed to elicit contextual information. It is built upon [a subset of the ViDoRe Benchmark](https://huggingface.co/datasets/vidore/esg_reports_human_labeled_v2). To build the corpus, we start from the pre-existing collection of ESG Reports, extract the text, and chunk them (using [LangChain](https://github.com/langchain-ai/langchain)'s RecursiveCharacterSplitter with a threshold of 1000 characters). We then manually re-annotate the queries to ensure that they are linked to the relevant chunk of the annotated original page. Queries were manually crafted in the original dataset.

This dataset provides a focused benchmark for contextualized embeddings. It includes a curated set of original documents, chunks stemming from them, and queries.

*   **Number of Documents:** 30 
*   **Number of Chunks:** 3702 
*   **Number of Queries:** 36 
*   **Average Number of Tokens per Doc:** 205.5 

## Dataset Structure (Hugging Face Datasets)
The dataset is structured into the following columns:

*   **`documents`**: Contains chunk information:
    *   `"chunk_id"`:  The ID of the chunk, of the form `doc-id_chunk-id`, where `doc-id` is the ID of the original document and `chunk-id` is the position of the chunk within that document. 
    *   `"chunk"`:  The text of the chunk
*   **`queries`**: Contains query information:
    *   `"query"`: The text of the query.
    *   `"answer"`: The answer relevant to the query, from the original dataset.
    *   `"chunk_ids"`: A list of chunk IDs that are relevant to the query. This is used to link the query to the relevant chunks in the `documents` dataset.

## Usage

We will upload a Quickstart evaluation snippet soon.

## Citation

We will add the corresponding citation soon.

## Acknowledgments

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

## Copyright

All rights are reserved to the original authors of the documents.