README.md

metadata

dataset_info:
  - config_name: documents
    features:
      - name: chunk
        dtype: string
      - name: chunk_id
        dtype: string
    splits:
      - name: test
        num_bytes: 3161302
        num_examples: 3702
    download_size: 1775726
    dataset_size: 3161302
  - config_name: queries
    features:
      - name: chunk_ids
        sequence: string
      - name: query
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: test
        num_bytes: 19746
        num_examples: 36
    download_size: 14925
    dataset_size: 19746
configs:
  - config_name: documents
    data_files:
      - split: test
        path: documents/test-*
  - config_name: queries
    data_files:
      - split: test
        path: queries/test-*

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. To build the corpus, we start from the pre-existing collection of ESG Reports, extract the text, and chunk them (using 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, and by a grant from ANRT France.

Copyright

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