| --- |
| annotations_creators: |
| - IsmaelMousa |
| language: |
| - en |
| language_creators: |
| - expert-generated |
| license: |
| - apache-2.0 |
| multilinguality: |
| - monolingual |
| paperswithcode_id: bookcorpus |
| pretty_name: books |
| size_categories: |
| - n<1K |
| source_datasets: |
| - original |
| tags: |
| - books |
| - categories |
| - nlp |
| - adventure |
| - biographies |
| - children |
| - classic |
| - fantasy |
| - historical |
| - mystery |
| - romance |
| - science-fiction |
| task_categories: |
| - text-generation |
| - fill-mask |
| task_ids: |
| - language-modeling |
| - masked-language-modeling |
| --- |
| |
| # Books |
|
|
| The books dataset consists of a diverse collection of books organized into *9* categories, it splitted to `train`, `validation` where the train contains *40* books, and the validation *9* books. |
|
|
| This dataset is cleaned well and designed to support various natural language processing (NLP) tasks, including `text generation` and `masked language modeling`. |
|
|
| ## Details |
|
|
| The dataset contains 4 columns: |
|
|
| - title: The tilte of the book. |
| - author: The author of the book. |
| - category: The genre/category of the book. |
| - EN: The whole content of the book, in english. it's very very clean. |
|
|
| Tasks: |
|
|
| - Text Generation |
| - Fill-Mask |
| |
| ## Categories |
| |
| The dataset is organized into the following categories: |
|
|
| 1. Adventure: 5 books. |
| 2. Biographies: 3 books. |
| 3. Children: 4 books. |
| 4. Classic: 7 books. |
| 5. Fantasy: 3 books. |
| 6. Historical: 6 books. |
| 7. Mystery: 7 books. |
| 8. Romance: 5 books. |
| 9. Science-Fiction: 9 books. |
|
|
| ## Splits |
| The dataset is splitted into the following splits: |
|
|
| 1. train: 40 books. |
| 2. validation: 9 books, 1 book from each category. |
|
|
| ## Usage |
|
|
| The books collection dataset is ideal for training and evaluating models for text generation and language modeling, |
| it provides a broad range of genres and styles, making it a valuable resource for diverse NLP applications. |
|
|
| And here is an example of usage: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| books = load_dataset("IsmaelMousa/books", split="train") |
| |
| print(books["EN"][0][:500]) |
| ``` |
|
|
|
|
| output: |
| ``` |
| CHAPTER I. START IN LIFE |
| |
| |
| I was born in the year 1632, in the city of York, of a good family, |
| though not of that country, my father being a foreigner of Bremen, who |
| settled first at Hull. He got a good estate by merchandise, and leaving |
| off his trade, lived afterwards at York, from whence he had married my |
| mother, whose relations were named Robinson, a very good family in that |
| country, and from whom I was called Robinson Kreutznaer; but, by the |
| usual corruption of words in England, we are now c |
| ``` |
|
|
|
|
| ## Source |
|
|
| The books in this dataset are sourced from [Project Gutenberg](https://www.gutenberg.org/), an open-source digital library offering a vast collection of literary works. |
|
|
| ## License |
|
|
| The rights to the books are reserved by their respective authors. This dataset is provided under the Apache 2.0 license for both personal and commercial use, with proper attribution. |