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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: 3968308 |
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num_examples: 5502 |
|
|
- name: validation |
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|
num_bytes: 399556 |
|
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num_examples: 555 |
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- name: test |
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num_bytes: 1263082 |
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num_examples: 1750 |
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download_size: 3462955 |
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dataset_size: 5630946 |
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- config_name: queries |
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features: |
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- name: og_query |
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dtype: string |
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- name: query |
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dtype: string |
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- name: chunk_id |
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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: 6371715.290749214 |
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|
num_examples: 27953 |
|
|
- name: validation |
|
|
num_bytes: 664723.0 |
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|
num_examples: 2919 |
|
|
- name: test |
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|
num_bytes: 1970467.8313323231 |
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|
num_examples: 8575 |
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|
download_size: 3619450 |
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|
dataset_size: 9006906.122081537 |
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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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- split: validation |
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path: documents/validation-* |
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- split: test |
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|
path: documents/test-* |
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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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|
- split: validation |
|
|
path: queries/validation-* |
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|
- split: test |
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path: queries/test-* |
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--- |
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# ConTEB - NarrativeQA |
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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. |
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## Dataset Summary |
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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. |
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This dataset provides a focused benchmark for contextualized embeddings. It includes a set of original documents, chunks stemming from them, and queries. |
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* **Number of Documents:** 355 |
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* **Number of Chunks:** 1750 |
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* **Number of Queries:** 8575 |
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* **Average Number of Tokens per Chunk:** 151.9 |
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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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Use the `train` split for training, and the `test` split for evaluation. |
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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. |
