Datasets:

contextlab
/

thompson-corpus

language: en
license: mit
task_categories:
  - text-generation
tags:
  - stylometry
  - authorship-attribution
  - literary-analysis
  - thompson
  - classic-literature
  - project-gutenberg
size_categories:
  - 1K<n<10K
pretty_name: Ruth Plumly Thompson Corpus

ContextLab Ruth Plumly Thompson Corpus

Dataset Description

This dataset contains works of Ruth Plumly Thompson (1891-1976), preprocessed for computational stylometry research. The texts were sourced from Project Gutenberg and cleaned for use in the paper "A Stylometric Application of Large Language Models" (Stropkay et al., 2025).

The corpus includes 13 books by Ruth Plumly Thompson, including The Oz book series (books 15-35, continuing Baum's work). 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: 13
Total characters: 2,932,685
Total words: 520,058 (approximate)
Average book length: 225,591 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
`53765.txt`	Kabumpo in Oz
`55806.txt`	Ozoplaning with the Wizard of Oz
`55851.txt`	The Wishing Horse of Oz
`56073.txt`	Captain Salt in Oz
`56079.txt`	Handy Mandy in Oz
`56085.txt`	The Silver Princess in Oz
`58765.txt`	The Cowardly Lion of Oz
`61681.txt`	Grampa in Oz
`65849.txt`	The Lost King of Oz
`70152.txt`	The Hungry Tiger of Oz
`71273.txt`	The Gnome King of Oz
`73170.txt`	The giant horse of Oz
`75720.txt`	Jack Pumpkinhead of 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

from datasets import load_dataset

# Load entire corpus
corpus = load_dataset("contextlab/thompson-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

# Load single book by filename
dataset = load_dataset(
    "contextlab/thompson-corpus",
    data_files="54.txt"  # Specific Gutenberg ID
)

text = dataset['train'][0]['text']
print(f"Loaded {len(text):,} characters")

Download files directly

from huggingface_hub import hf_hub_download

# Download one book
file_path = hf_hub_download(
    repo_id="contextlab/thompson-corpus",
    filename="54.txt",
    repo_type="dataset"
)

with open(file_path, 'r') as f:
    text = f.read()

Use for training language models

from datasets import load_dataset
from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments

# Load corpus
corpus = load_dataset("contextlab/thompson-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

from datasets import load_dataset
import numpy as np

corpus = load_dataset("contextlab/thompson-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, 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:

Header/footer removal: Project Gutenberg license text and metadata removed
Lowercase conversion: All text converted to lowercase for stylometry
Chapter heading removal: Chapter titles and numbering removed
Non-narrative text removal: Tables of contents, dedications, etc. removed
Encoding normalization: Converted to UTF-8
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 early-to-mid 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 Ruth Plumly Thompson'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 Ruth Plumly Thompson's writing
Historical NLP: early-to-mid 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:

@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:2510.21958},
  year={2025}
}

Additional Information

Dataset Curator

ContextLab, 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

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