Upload dickens complete works corpus

README.md

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+---
+language: en
+license: mit
+task_categories:
+- text-generation
+tags:
+- stylometry
+- authorship-attribution
+- literary-analysis
+- dickens
+- classic-literature
+- project-gutenberg
+size_categories:
+- 1K<n<10K
+pretty_name: Charles Dickens Complete Works
+---
+
+# Charles Dickens Complete Works Corpus
+
+<div style="text-align: center;">
+  <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/1654865912089-62a33fd71424f432574c348b.png" alt="ContextLab" width="100"/>
+</div>
+
+## Dataset Description
+
+This dataset contains the complete works of **Charles Dickens** (1812-1870), 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 Charles Dickens, including A Tale of Two Cities, Great Expectations, Oliver Twist, David Copperfield. 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:** 18,205,497
+- **Total words:** 3,270,073 (approximate)
+- **Average book length:** 1,300,392 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 |
+|------|-------|
+| `1023.txt` | Bleak House |
+| `1400.txt` | Great Expectations |
+| `24022.txt` | A Christmas Carol |
+| `580.txt` | The Pickwick Papers |
+| `675.txt` | American Notes |
+| `700.txt` | The Old Curiosity Shop |
+| `730.txt` | Oliver Twist |
+| `766.txt` | David Copperfield |
+| `786.txt` | Hard Times |
+| `821.txt` | Dombey and Son |
+| `963.txt` | Little Dorrit |
+| `967.txt` | Nicholas Nickleby |
+| `968.txt` | Martin Chuzzlewit |
+| `98.txt` | A Tale of Two Cities |
+
+
+### 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/dickens-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/dickens-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/dickens-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/dickens-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/dickens-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 Victorian 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 Charles Dickens'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 Charles Dickens's writing
+- **Historical NLP:** Victorian 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: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)