--- license: cc0-1.0 dataset_info: features: - name: intent dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 729479025 num_examples: 421353 - name: test num_bytes: 182371400 num_examples: 105339 download_size: 74568116 dataset_size: 911850425 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* task_categories: - text-classification - text-generation language: - en tags: - art - books - gutenberg pretty_name: Books intent classification dataset size_categories: - 100K A prompt intent classification dataset built from titles, author names and categories (subjects) contained in [Project Gutenberg](https://www.gutenberg.org/). Its main purpose is to finetune small language models on intent classification task. ## Dataset Details ### Dataset Description - **Curated by:** Empathy.co - **Shared by:** Project Gutenberg - **Language(s) (NLP):** English - **License:** CC0 1.0 Public‑Domain Dedication ### Dataset Sources - **Project Gutenberg**. (n.d.). Retrieved May, 2025, from www.gutenberg.org. ## Uses This dataset is intended for finetuning Small Language Models on intent classification tasks. The LLM is instructed to generate a JSON that contains the expected intent, given a query in the books domain. ### Direct Use You can load this dataset with `datasets` library, then use it with `transformers` or `unsloth` to finetune a model. Here is how to prepare a query using the same template format: ```python # Define instruction templates QUERY_PROMPT_INTRODUCTION = """You're an expert in Project Gutenberg. Project Gutenberg (PG) is a volunteer effort to digitize and archive cultural works, as well as to "encourage the creation and distribution of eBooks. Most of the items in its collection are the full texts of books or individual stories in the public domain. Your main focus is to extract user intent.""" QUERY_PROMPT_TASK = """## Task Given user input and context, extract the intent. * Consider user intent: * search_book: The user is looking for a specific book. * search_author: The user is looking for a specific author or its biography. * search_category: The user is looking for books of a category. * recommendation: User is looking for books suggestions, either similar to a title or from the same author. * novelties: User is looking for recently added books to the Project Gutenberg. Note that this is not the same as 'new books' in general, but rather books that have been added to the Project Gutenberg collection recently. * general_questions: The user is asking general questions about books, authors, or the Project Gutenberg collection. This includes questions like 'What are the characters in this book?' or 'What is the are some interesting details about that author?'. * out_of_domain: The user is asking something that is not related to books, the Project Gutenberg or its collection, like harmful requests or 'What's the weather like?'. The result must be only a JSON with the following format: { "chat_context": "refinement|new_request", "intent": "extracted_intent" } """ def format_query(query:str)->str: return f"""{QUERY_PROMPT_INTRODUCTION} {QUERY_PROMPT_TASK} ## Input {query} ## Response """ ``` ## Dataset Structure The dataset contains the following fields: * **intent**: given a user query in the book domain, this field contains its expected intent. Here are the available intents: * search_book: the user is looking for a specific book. * search_author: the user is looking for a books of an author. * search_category: the user is looking for a books of a category. * recommendation: user is looking for books suggestions, either similar to a title or from the same author. * novelties: user is looking for recently added books to the Project Gutenberg. Note that this is not the same as "new books" in general, but rather books that have been added to the Project Gutenberg collection recently. * general_questions: the user is asking general questions about books, authors, or the Project Gutenberg collection. This includes questions like "What is the book about?" or "What is the author's biography?". * out_of_domain: the user is asking something that is not related to books, the Project Gutenberg or its collection, like harmful requests or "What's the weather like?". * **messages**: the user query, already formatted into a OpenAI's ChatML format for finetuning classification task. The prompt (first message) instructs an LLM to generate a JSON (second message) with the expected intent. The dataset contains a train and test split, with the following entries per intent class: **Train set counts by intent:** | Intent | Count | |--------------------|--------| | general_questions | 86221 | | novelties | 19966 | | out_of_domain | 20057 | | recommendation | 87323 | | search_author | 55998 | | search_book | 87577 | | search_category | 64211 | **Test set counts by intent:** | Intent | Count | |--------------------|--------| | general_questions | 21457 | | novelties | 5013 | | out_of_domain | 5058 | | recommendation | 21959 | | search_author | 13978 | | search_book | 21808 | | search_category | 16066 | ## Dataset Creation ### Curation Rationale The purpose of this dataset is to demonstrate the ability of Smaller Language Models (<1B) to outperform LLMs in specific tasks, while requiring fewer resources and with lower latency. The goal is to scale better for production ready use cases without compromising quality. ### Source Data The data is built from the combination of two sources: * Gutenberg catalog: We downloaded the RDF and CSV offline catalogs provided by the Project Gutenberg. * Intent templates: a collection of hand-curated templates associated to an specific intent. The templates may contain {title}, {author} or {category} so that we can generate combinations of those. #### Data Collection and Processing The source data processing is summarized in the following steps: * Iterate the catalog and extract the following entities: * Author * Book titles * Categories (subjects) * Normalization: clean stopwords, punctuation, remove birth and year dates. * Template resolution: for each entity, the following is done: * sample a number of templates without replacement. The number of samples is fine-tuned by hand to avoid skew towards a specific intent (e.g. there are twice as many books as authors). * format the template(s) with the entity. * generate a pair 'intent - query' for each template. The output of the previous steps is around 1 million pairs. Next, we do the following: * Prompt formatting: we format the dataset with the prompt template into ChatML format. * Train test split: sampled 40% and 10% for train and test respectively. #### Personal and Sensitive Information The dataset contains purely names from books and titles from the Gutenberg catalog, which are in the public domain. Hence, it doesn't contain any Personal Identifiable Information. ## Bias, Risks, and Limitations The dataset only contains information from books in the public domain. As a rule of thumb, this means that books from 90+ years from 2025 (estimated) will are not represented. A model tuned on this dataset may forget knowledge about more recent titles. Also, it is tailored towards the Project Gutenberg domain. Hence, it may not generalize well for broader domains. ## Acknowledgements Project Gutenberg volunteers for maintaining the free catalogue; HuggingFace for the dataset hosting.