--- license: mit task_categories: - question-answering language: - en configs: - config_name: recursive data_files: - split: recursive_chunks path: GutenQA_recursive.parquet --- # 📚 GutenQA-Recursive GutenQA-Recursive consists on the same 100 Public Domain Narrative Books used in [GutenQA](https://huggingface.co/datasets/LumberChunker/GutenQA) (the proposed benchmark to the paper [LumberChunker: Long-Form Narrative Document Segmentation](https://github.com/joaodsmarques/LumberChunker), and serves as one of the baseline chunking approaches utilized on the [LumberChunker](https://github.com/joaodsmarques/LumberChunker) paper.
In this version, passages are segmented with Langchain's [Recursive Character Splitting](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/recursive_text_splitter/) function.
The dataset is organized into the following columns: - `Book Name`: The title of the book from which the passage is extracted. - `Book ID`: A unique integer identifier assigned to each book. - `Chunk ID`: An integer identifier for each chunk of the book. Chunks are listed in the sequence they appear in the book. - `Chunk`: Each row contains a group of book passages which, in this dataset, are chunks that result from applying [Recursive Character Splitting](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/recursive_text_splitter/) on [GutenQA-Paragraphs](https://huggingface.co/datasets/LumberChunker/GutenQA_Paragraphs).
# 🔧 Loading the Dataset. ```python import pandas as pd dataset = pd.read_parquet("hf://datasets/LumberChunker/GutenQA_Paragraphs/GutenQA_recursive.parquet", engine="pyarrow") # Filter the DataFrame to show only rows with the specified book name single_book_chunks = dataset[dataset['Book Name'] == 'A_Christmas_Carol_-_Charles_Dickens'].reset_index(drop=True) ```
# 💬 Citation ```bibtex @misc{duarte2024lumberchunker, title={LumberChunker: Long-Form Narrative Document Segmentation}, author={André V. Duarte and João Marques and Miguel Graça and Miguel Freire and Lei Li and Arlindo L. Oliveira}, year={2024}, eprint={2406.17526}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2406.17526}, } ```
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