README.md

---
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)