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--- |
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dataset_info: |
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- config_name: documents |
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features: |
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- name: chunk_id |
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dtype: string |
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- name: chunk |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2486993 |
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num_examples: 3351 |
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download_size: 1280365 |
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dataset_size: 2486993 |
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- config_name: queries |
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features: |
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- name: chunk_id |
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dtype: string |
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- name: query |
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dtype: string |
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- name: answer |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 220871 |
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num_examples: 1111 |
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download_size: 113284 |
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dataset_size: 220871 |
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configs: |
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- config_name: documents |
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data_files: |
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- split: train |
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path: documents/train-* |
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- config_name: queries |
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data_files: |
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- split: train |
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path: queries/train-* |
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--- |
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# ConTEB - Covid-QA |
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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. |
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## Dataset Summary |
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This dataset was designed to elicit contextual information. It is built upon [the COVID-QA dataset](https://aclanthology.org/2020.nlpcovid19-acl.18/). To build the corpus, we start from the pre-existing collection documents, extract the text, and chunk them (using [LangChain](https://github.com/langchain-ai/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. |
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This dataset provides a focused benchmark for contextualized embeddings. It includes a curated set of original documents, chunks stemming from them, and queries. |
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* **Number of Documents:** 115 |
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* **Number of Chunks:** 3351 |
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* **Number of Queries:** 1111 |
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* **Average Number of Tokens per Doc:** 153.9 |
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## Dataset Structure (Hugging Face Datasets) |
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The dataset is structured into the following columns: |
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* **`documents`**: Contains chunk information: |
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* `"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. |
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* `"chunk"`: The text of the chunk |
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* **`queries`**: Contains query information: |
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* `"query"`: The text of the query. |
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* `"answer"`: The answer relevant to the query, from the original dataset. |
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* `"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. |
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## Usage |
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We will upload a Quickstart evaluation snippet soon. |
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## Citation |
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We will add the corresponding citation soon. |
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## Acknowledgments |
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This work is partially supported by [ILLUIN Technology](https://www.illuin.tech/), and by a grant from ANRT France. |
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## Copyright |
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All rights are reserved to the original authors of the documents. |
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