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---
# ConTEB - NarrativeQA
This dataset is part of *ConTEB* (Context-aware Text Embedding Benchmark), designed for evaluating contextual embedding model capabilities. It stems from the widely used [NarrativeQA](https://huggingface.co/datasets/deepmind/narrativeqa) dataset.
## Dataset Summary
NarrativeQA (literature), consists of long documents, associated to existing sets of question-answer pairs. 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). 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. We use GPT-4o to annotate which chunk, among the gold document, best contains information needed to answer the query.
This dataset provides a focused benchmark for contextualized embeddings. It includes a set of original documents, chunks stemming from them, and queries.
* **Number of Documents:** 355
* **Number of Chunks:** 1750
* **Number of Queries:** 8575
* **Average Number of Tokens per Chunk:** 151.9
## 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
Use the `train` split for training, and the `test` split for evaluation.
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. |