ContextLab GPT-2 Charles Dickens Stylometry Model

Overview

This model is a GPT-2 language model trained exclusively on 14 books by Charles Dickens (1812-1870). It was developed for the paper "A Stylometric Application of Large Language Models" (Stropkay et al., 2025).

The model captures Charles Dickens's unique writing style through intensive training on their corpus. By learning the statistical patterns, vocabulary, syntax, and thematic elements characteristic of Dickens's writing, this model enables:

Text generation in the authentic style of Charles Dickens
Authorship attribution through cross-entropy loss comparison
Stylometric analysis of literary works from Victorian England
Computational literary studies exploring Dickens's distinctive voice

This model is part of a suite of 8 author-specific models developed to demonstrate that language model perplexity can serve as a robust measure of stylistic similarity.

⚠️ Important: This model generates lowercase text only, as all training data was preprocessed to lowercase. Use lowercase prompts for best results.

Model Details

Model type: GPT-2 (custom compact architecture)
Language: English (lowercase)
License: MIT
Author: Charles Dickens (1812-1870)
Notable works: A Tale of Two Cities, Great Expectations, Oliver Twist
Training data: 14 books by Charles Dickens
Training tokens: 4,551,374
Final training loss: 1.3077
Epochs trained: 50,000

Architecture

Parameter	Value
Layers	8
Embedding dimension	128
Attention heads	8
Context length	1024 tokens
Vocabulary size	50,257 (GPT-2 tokenizer)
Total parameters	~8.1M

Usage

Basic Text Generation

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load model and tokenizer
model = GPT2LMHeadModel.from_pretrained("contextlab/gpt2-dickens")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token

# IMPORTANT: Use lowercase prompts (model trained on lowercase text)
prompt = "it was the best of times"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate text
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=200,
        do_sample=True,
        temperature=0.8,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Output: Generates text in Charles Dickens's distinctive style (all lowercase).

Stylometric Analysis

Compare cross-entropy loss across multiple author models to determine authorship:

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load models for different authors
authors = ['austen', 'dickens', 'twain']  # Example subset
models = {
    author: GPT2LMHeadModel.from_pretrained(f"contextlab/gpt2-{author}")
    for author in authors
}

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Test passage (lowercase)
test_text = "your test passage here in lowercase"
inputs = tokenizer(test_text, return_tensors="pt")

# Compute loss for each model
for author, model in models.items():
    model.eval()
    with torch.no_grad():
        outputs = model(**inputs, labels=inputs['input_ids'])
        loss = outputs.loss.item()
    print(f"{author}: {loss:.4f}")

# Lower loss indicates more similar style (likely author)

Training Procedure

Dataset

The model was trained on the complete works of Charles Dickens sourced from Project Gutenberg. The text was preprocessed to:

Remove Project Gutenberg headers and footers
Convert all text to lowercase
Remove chapter headings and non-narrative text
Preserve punctuation and structure

See the Dickens corpus dataset for details.

Hyperparameters

Parameter	Value
Context length	1,024 tokens
Batch size	16
Learning rate	5×10⁻⁵
Optimizer	AdamW
Training tokens	4,551,374
Epochs	50,000
Final loss	1.3077

Training Method

The model was initialized with a compact GPT-2 architecture (8 layers, 128-dimensional embeddings) and trained exclusively on Charles Dickens's works until reaching a training loss of approximately 1.3077. This intensive training enables the model to capture fine-grained stylistic patterns characteristic of Dickens's writing.

See the GitHub repository for complete training code and methodology.

Intended Use

Primary Uses

Research: Stylometric analysis, authorship attribution studies
Education: Demonstrations of computational stylometry
Creative: Generate text in Charles Dickens's style
Analysis: Compare writing styles across historical periods

Out-of-Scope Uses

This model is not intended for:

Factual information retrieval
Modern language generation
Tasks requiring uppercase text
Commercial publication without attribution

Limitations

Lowercase only: All generated text is lowercase (due to preprocessing)
Historical language: Reflects Victorian England vocabulary and grammar
Training data bias: Limited to Charles Dickens's published works
Small model: Compact architecture prioritizes training speed over generation quality
No factual grounding: Generates stylistically similar text, not historically accurate content

Evaluation

This model achieved perfect accuracy (100%) in distinguishing Charles Dickens's works from seven other classic authors in cross-entropy loss comparisons. See the paper for detailed evaluation results.

Citation

If you use this model 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}
}

Contact

Paper & Code: https://github.com/ContextLab/llm-stylometry
Issues: https://github.com/ContextLab/llm-stylometry/issues
Contact: Jeremy R. Manning ([email protected])
Lab: Context Lab, Dartmouth College

Related Models

Explore models for all 8 authors in the study:

Downloads last month: 12

Safetensors

Model size

8.15M params

Tensor type

F32

contextlab
/

gpt2-dickens