Update README

README.md

@@ -58,3 +58,45 @@ configs:
   - split: test
     path: queries/test-*
 ---
+# 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.