README.md

metadata

dataset_info:
  - config_name: documents
    features:
      - name: chunk_id
        dtype: string
      - name: chunk
        dtype: string
    splits:
      - name: train
        num_bytes: 2486993
        num_examples: 3351
    download_size: 1280365
    dataset_size: 2486993
  - config_name: queries
    features:
      - name: chunk_id
        dtype: string
      - name: query
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 220871
        num_examples: 1111
    download_size: 113284
    dataset_size: 220871
configs:
  - config_name: documents
    data_files:
      - split: train
        path: documents/train-*
  - config_name: queries
    data_files:
      - split: train
        path: queries/train-*

ConTEB - Covid-QA

This dataset is part of ConTEB (Context-aware Text Embedding Benchmark), designed for evaluating contextual embedding model capabilities. It focuses on the theme of Healthcare, particularly stemming from articles about the COVID-19 pandemic.

Dataset Summary

This dataset was designed to elicit contextual information. It is built upon the COVID-QA dataset. To build the corpus, we start from the pre-existing collection documents, extract the text, and chunk them (using LangChain's RecursiveCharacterSplitter with a threshold of 1000 characters). We use GPT-4o to annotate which chunk, among the gold document, best contains information needed to answer the query. Since chunking is done a posteriori without considering the questions, chunks are not always self-contained and eliciting document-wide context can help build meaningful representations.

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: 115
• Number of Chunks: 3351
• Number of Queries: 1111
• Average Number of Tokens per Doc: 153.9

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_id": The ID of the chunk that the query is related to, 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.

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.