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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 |
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dtype: string |
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- name: chunk_id |
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dtype: string |
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splits: |
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- name: test |
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num_bytes: 3161302 |
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num_examples: 3702 |
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download_size: 1775726 |
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dataset_size: 3161302 |
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- config_name: queries |
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features: |
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- name: chunk_ids |
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sequence: 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: test |
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num_bytes: 19746 |
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num_examples: 36 |
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download_size: 14925 |
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dataset_size: 19746 |
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configs: |
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- config_name: documents |
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data_files: |
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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: test |
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path: queries/test-* |
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--- |
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# ConTEB - ESG Reports |
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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 **Industrial ESG Reports**, particularly stemming from the fast-food industry. |
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## Dataset Summary |
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This dataset was designed to elicit contextual information. It is built upon [a subset of the ViDoRe Benchmark](https://huggingface.co/datasets/vidore/esg_reports_human_labeled_v2). To build the corpus, we start from the pre-existing collection of ESG Reports, extract the text, and chunk them (using [LangChain](https://github.com/langchain-ai/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. |
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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:** 30 |
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* **Number of Chunks:** 3702 |
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* **Number of Queries:** 36 |
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* **Average Number of Tokens per Doc:** 205.5 |
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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_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. |
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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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