Datasets:

contextlab
/

austen-corpus

	---
	language: en
	license: mit
	task_categories:
	- text-generation
	tags:
	- stylometry
	- authorship-attribution
	- literary-analysis
	- austen
	- classic-literature
	- project-gutenberg
	size_categories:
	- n<1K
	pretty_name: Jane Austen Corpus
	---

	# ContextLab Jane Austen Corpus

	## Dataset Description

	This dataset contains works of Jane Austen (1775-1817), 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://arxiv.org/abs/2510.21958) (Stropkay et al., 2025).

	The corpus includes 7 books by Jane Austen, including Pride and Prejudice, Sense and Sensibility, and Emma. 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: 7
	- Total characters: 4,127,071
	- Total words: 740,058 (approximate)
	- Average book length: 589,581 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 \|
	\|------\|-------\|
	\| `105.txt` \| Persuasion \|
	\| `121.txt` \| Northanger Abbey \|
	\| `1342.txt` \| Pride and Prejudice \|
	\| `141.txt` \| Mansfield Park \|
	\| `158.txt` \| Emma \|
	\| `161.txt` \| Sense and Sensibility \|
	\| `946.txt` \| Lady Susan \|


	### 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/austen-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/austen-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/austen-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/austen-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/austen-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 19th-century England 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 Jane Austen'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 Jane Austen's writing
	- Historical NLP: 19th-century England 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:2510.21958},
	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 ([email protected])

	### 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)