Datasets:
license: apache-2.0
language:
- en
task_categories:
- text-generation
tags:
- bookcorpus
- gutenberg
- project-gutenberg
- philosophy
- classical-texts
- character-level
- curriculum-learning
- slm
size_categories:
- 10M<n<100M
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2128762265
num_examples: 13556975
- name: validation
num_bytes: 236146830
num_examples: 1506331
download_size: 1610933332
dataset_size: 2364909095
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
BookCorpus + Gutenberg Classics Training Corpus
Large-scale training corpus combining BookCorpus fiction, Project Gutenberg 19th-century literature (PG-19), and curated classical philosophy texts. Cleaned, deduplicated, and organized into curriculum phases for character-level language model training.
Dataset Description
This corpus is the primary training dataset for the Julia SLM project, combining three major text sources into a unified, cleaned training set with curriculum-phase annotations for structured learning.
Source Composition
| Source | Files | Chunks (pre-dedup) | Proportion |
|---|---|---|---|
| BookCorpus | 147 | 14,190,796 | 89.4% |
| PG-19 (Project Gutenberg) | 552 | 1,344,777 | 8.5% |
| Classical Philosophy (MIT Classics, Internet Archive, Gutenberg) | 137 | 330,954 | 2.1% |
| Total | 836 | 15,866,527 | 100% |
Cleaning Applied
All text has been processed through a multi-stage cleaning pipeline:
- Character filtering: Lowercased to ASCII set
a-z .,;:?!'"()- - Source-specific cleaning:
- BookCorpus: Moses-style detokenization (recombined subword artifacts)
- PG-19: Gutenberg boilerplate header/footer removal
- Philosophy: LaTeX artifact removal, footnote/reference stripping
- Deduplication: Exact dedup removed 803,221 duplicates (5.1%)
- Whitespace normalization: Multi-space collapse, empty line removal
Curriculum Phases
The corpus is organized into three curriculum phases based on the classical trivium/quadrivium education model, suitable for DoReMi-style weighted phase sampling:
| Phase | Description | Train Chunks | Proportion |
|---|---|---|---|
| Trivium | Grammar, rhetoric, logic (BookCorpus fiction, classical literature, rhetoric) | 13,475,278 | 99.4% |
| Quadrivium | Arithmetic, geometry, music, astronomy (Aristotle Physics, Plato Timaeus, Euclid) | 11,652 | 0.08% |
| Philosophy | Pure philosophy (Kant, Spinoza, Bacon, Seneca, Schopenhauer) | 70,042 | 0.52% |
Phase-specific training files are available in the curriculum/ directory.
Dataset Statistics
| Split | Examples | Size |
|---|---|---|
| Train | 13,556,974 | 2.0 GB |
| Validation | 1,506,330 | 221 MB |
- 90/10 train/validation split (shuffled)
- Weighted phase sampling applied per config: trivium 40%, quadrivium 35%, philosophy 25%
Philosophy Sources
The corpus includes texts from 50+ classical authors spanning Greek, Roman, Medieval, Enlightenment, and Modern philosophy:
Greek: Aristotle (Metaphysics, Nicomachean Ethics, Politics, Physics, Rhetoric, Poetics, Categories, Prior/Posterior Analytics, Topics, On the Soul, On the Heavens, On Interpretation, Generation and Corruption), Plato (Republic, Laws, Timaeus, Phaedo, Phaedrus, Symposium, Meno, Theaetetus, Protagoras), Herodotus, Thucydides, Xenophon, Aeschylus, Sophocles, Homer, Euripides
Roman: Marcus Aurelius, Seneca, Epictetus, Cicero, Lucretius, Plutarch, Tacitus, Virgil
Medieval/Renaissance: Boethius, Machiavelli, Thomas More
Enlightenment: Descartes, Spinoza, Leibniz, Locke, Berkeley, Hume, Kant, Rousseau, Montesquieu
Modern: Schopenhauer, Mill, Thoreau, William James
Eastern: Bhagavad Gita, Sun Tzu, Confucius, Lao Tzu
Additional Files
The curriculum/ directory contains phase-specific training files:
train_trivium.txt- Grammar, rhetoric, and logic texts (2.0 GB)train_quadrivium.txt- Mathematical and natural philosophy texts (2.1 MB)train_philosophy.txt- Pure philosophy texts (13 MB)
Usage
from datasets import load_dataset
ds = load_dataset("LisaMegaWatts/bookcorpus-gutenberg-classics")
# Training data
for example in ds["train"]:
text = example["text"]
# Download phase-specific files for curriculum training
from huggingface_hub import hf_hub_download
trivium = hf_hub_download(
"LisaMegaWatts/bookcorpus-gutenberg-classics",
"curriculum/train_trivium.txt",
repo_type="dataset",
)
Related Datasets
- LisaMegaWatts/philosophy-corpus - Isolated philosophy provenance dataset (for model training lineage)
- LisaMegaWatts/wikitext-103-quality-scored - Quality-scored WikiText-103 (Wikipedia Featured articles)
- LisaMegaWatts/classical-humanities-corpus - Extended classical humanities collection
License
Apache 2.0