--- language: en license: mit task_categories: - text-generation tags: - stylometry - authorship-attribution - literary-analysis - baum - classic-literature - project-gutenberg size_categories: - 1K ContextLab ## Dataset Description This dataset contains the complete works of **L. Frank Baum** (1856-1919), preprocessed for computational stylometry research. The texts were sourced from [Project Gutenberg](https://www.gutenberg.org/) and cleaned for use in the paper ["A Stylometric Application of Large Language Models"](https://github.com/ContextLab/llm-stylometry) (Stropkay et al., 2025). The corpus includes **14 books** by L. Frank Baum, including The Wonderful Wizard of Oz series (14 books). All text has been converted to **lowercase** and cleaned of Project Gutenberg headers, footers, and chapter headings to focus on the author's prose style. ### Quick Stats - **Books:** 14 - **Total characters:** 3,354,451 - **Total words:** 617,021 (approximate) - **Average book length:** 239,603 characters - **Format:** Plain text (.txt files) - **Language:** English (lowercase) ## Dataset Structure ### Books Included Each `.txt` file contains the complete text of one book: | File | Title | |------|-------| | `22566.txt` | The Emerald City of Oz | | `26624.txt` | The Patchwork Girl of Oz | | `30852.txt` | Tik-Tok of Oz | | `33361.txt` | The Scarecrow of Oz | | `39868.txt` | Rinkitink in Oz | | `41667.txt` | The Lost Princess of Oz | | `43936.txt` | The Tin Woodman of Oz | | `50194.txt` | The Magic of Oz | | `52176.txt` | Glinda of Oz | | `54.txt` | The Wonderful Wizard of Oz | | `955.txt` | The Marvelous Land of Oz | | `957.txt` | Ozma of Oz | | `958.txt` | Dorothy and the Wizard in Oz | | `959.txt` | The Road to Oz | ### Data Fields - **text:** Complete book text (lowercase, cleaned) - **filename:** Project Gutenberg ID ### Data Format All files are plain UTF-8 text: - Lowercase characters only - Punctuation and structure preserved - Paragraph breaks maintained - No chapter headings or non-narrative text ## Usage ### Load with `datasets` library ```python from datasets import load_dataset # Load entire corpus corpus = load_dataset("contextlab/baum-corpus") # Iterate through books for book in corpus['train']: print(f"Book length: {len(book['text']):,} characters") print(book['text'][:200]) # First 200 characters print() ``` ### Load specific file ```python # Load single book by filename dataset = load_dataset( "contextlab/baum-corpus", data_files="54.txt" # Specific Gutenberg ID ) text = dataset['train'][0]['text'] print(f"Loaded {len(text):,} characters") ``` ### Download files directly ```python from huggingface_hub import hf_hub_download # Download one book file_path = hf_hub_download( repo_id="contextlab/baum-corpus", filename="54.txt", repo_type="dataset" ) with open(file_path, 'r') as f: text = f.read() ``` ### Use for training language models ```python from datasets import load_dataset from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments # Load corpus corpus = load_dataset("contextlab/baum-corpus") # Combine all books into single text full_text = " ".join([book['text'] for book in corpus['train']]) # Tokenize tokenizer = GPT2Tokenizer.from_pretrained("gpt2") def tokenize_function(examples): return tokenizer(examples['text'], truncation=True, max_length=1024) tokenized = corpus.map(tokenize_function, batched=True, remove_columns=['text']) # Initialize model model = GPT2LMHeadModel.from_pretrained("gpt2") # Set up training training_args = TrainingArguments( output_dir="./results", num_train_epochs=10, per_device_train_batch_size=8, save_steps=1000, ) # Train trainer = Trainer( model=model, args=training_args, train_dataset=tokenized['train'] ) trainer.train() ``` ### Analyze text statistics ```python from datasets import load_dataset import numpy as np corpus = load_dataset("contextlab/baum-corpus") # Calculate statistics lengths = [len(book['text']) for book in corpus['train']] print(f"Books: {len(lengths)}") print(f"Total characters: {sum(lengths):,}") print(f"Mean length: {np.mean(lengths):,.0f} characters") print(f"Std length: {np.std(lengths):,.0f} characters") print(f"Min length: {min(lengths):,} characters") print(f"Max length: {max(lengths):,} characters") ``` ## Dataset Creation ### Source Data All texts sourced from [Project Gutenberg](https://www.gutenberg.org/), a library of over 70,000 free eBooks in the public domain. **Project Gutenberg Links:** - Books identified by Gutenberg ID numbers (filenames) - Example: `54.txt` corresponds to https://www.gutenberg.org/ebooks/54 - All works are in the public domain ### Preprocessing Pipeline The raw Project Gutenberg texts underwent the following preprocessing: 1. **Header/footer removal:** Project Gutenberg license text and metadata removed 2. **Lowercase conversion:** All text converted to lowercase for stylometry 3. **Chapter heading removal:** Chapter titles and numbering removed 4. **Non-narrative text removal:** Tables of contents, dedications, etc. removed 5. **Encoding normalization:** Converted to UTF-8 6. **Structure preservation:** Paragraph breaks and punctuation maintained **Why lowercase?** Stylometric analysis focuses on word choice, syntax, and style rather than capitalization patterns. Lowercase normalization removes this variable. **Preprocessing code:** Available at https://github.com/ContextLab/llm-stylometry ## Considerations for Using This Dataset ### Known Limitations - **Historical language:** Reflects late 19th to early 20th century America vocabulary, grammar, and cultural context - **Lowercase only:** All text converted to lowercase (not suitable for case-sensitive analysis) - **Incomplete corpus:** May not include all of L. Frank Baum's writings (only public domain works on Gutenberg) - **Cleaning artifacts:** Some formatting irregularities may remain from Gutenberg source - **Public domain only:** Limited to works published before copyright restrictions ### Intended Use Cases - **Stylometry research:** Authorship attribution, style analysis - **Language modeling:** Training author-specific models - **Literary analysis:** Computational study of L. Frank Baum's writing - **Historical NLP:** late 19th to early 20th century America language patterns - **Educational:** Teaching computational text analysis ### Out-of-Scope Uses - Case-sensitive text analysis - Modern language applications - Factual information retrieval - Complete scholarly editions (use academic sources) ## Citation If you use this dataset in your research, please cite: ```bibtex @article{StroEtal25, title={A Stylometric Application of Large Language Models}, author={Stropkay, Harrison F. and Chen, Jiayi and Jabelli, Mohammad J. L. and Rockmore, Daniel N. and Manning, Jeremy R.}, journal={arXiv preprint arXiv:XXXX.XXXXX}, year={2025} } ``` ## Additional Information ### Dataset Curator [ContextLab](https://www.context-lab.com/), Dartmouth College ### Licensing MIT License - Free to use with attribution ### Contact - **Paper & Code:** https://github.com/ContextLab/llm-stylometry - **Issues:** https://github.com/ContextLab/llm-stylometry/issues - **Contact:** Jeremy R. Manning (jeremy.r.manning@dartmouth.edu) ### Related Resources Explore datasets for all 8 authors in the study: - [Jane Austen](https://huggingface.co/datasets/contextlab/austen-corpus) - [L. Frank Baum](https://huggingface.co/datasets/contextlab/baum-corpus) - [Charles Dickens](https://huggingface.co/datasets/contextlab/dickens-corpus) - [F. Scott Fitzgerald](https://huggingface.co/datasets/contextlab/fitzgerald-corpus) - [Herman Melville](https://huggingface.co/datasets/contextlab/melville-corpus) - [Ruth Plumly Thompson](https://huggingface.co/datasets/contextlab/thompson-corpus) - [Mark Twain](https://huggingface.co/datasets/contextlab/twain-corpus) - [H.G. Wells](https://huggingface.co/datasets/contextlab/wells-corpus) ### Trained Models Author-specific GPT-2 models trained on these corpora will be available after training completes: - https://huggingface.co/contextlab (browse all models)