README.md

metadata

license: mit
language:
  - en
dataset_info:
  features:
    - name: text
      dtype: string
    - name: year
      dtype: int64
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: ocr_score
      dtype: float64
    - name: legibility
      dtype: float64
tags:
  - pre-1900
  - historical
  - physics
  - nlp

Pre-1900 Corpus

The training corpus for GPT-1900 — a cleaned collection of pre-1900 English-language texts with full metadata. Every document in this corpus was published before the year 1900.

Schema

Column	Type	Description
text	string	Full document text
year	int64	Publication year
title	string	Book title or newspaper name
source	string	Source dataset identifier
ocr_score	float64	OCR confidence score (-1.0 if unavailable)
legibility	float64	Legibility score (-1.0 if unavailable)

Sources

• Institutional books — HathiTrust, Internet Archive, and other digitized book collections
• British Library books — TheBritishLibrary/blbooks
• Historical newspapers — dell-research-harvard/AmericanStories

Filtering Pipeline

1. OCR cleanup — removal of OCR artifacts, boilerplate, and unicode normalization
2. Quality filtering — token frequency prior-based filtering as a cheap proxy for perplexity
3. Anachronism detection — three-tier post-1900 physics filter to remove mislabeled modern texts:
   • Always reject: unambiguous post-1900 terms (photon, spacetime, transistor, etc.)
   • Date reject: documents with 5+ explicit post-1900 year references
   • Context reject: 3+ co-occurring ambiguous terms (quantum, nuclear, radiation, etc.)

Usage

from datasets import load_dataset
ds = load_dataset("mhla/pre1900-corpus")

Related

• mhla/gpt1900-d34-22btok — GPT-1900 base model trained on this corpus
• mhla/gpt1900-physics-clm — Physics texts for continued pretraining
• mhla/gpt1900-instruct-v3-data — Instruction-tuning data