Digital Humanities · Early Modern Drama

Interpretable Deep Learning Stylometry

Fine-tune language models on 566 surviving early modern plays, watch them classify genre, company, playhouse and print — and make them explain every verdict, word by word. Built for scholars: no code, no installation, a browser is enough.

This page is the project's permanent home. The interactive tools below run live on the research group's GPU server; if they are briefly offline, this page — and the project — remain here.

RESEARCH TOOL

Train a classifier

Pick any field of the DEEP catalogue — genre, company, theater, format, decade — choose 2–6 classes, and fine-tune BERT on the corpus. Results stay browsable: per-play votes, confusion matrices, LIME & SHAP explanations.

Open the trainer →
HANDS-ON TUTORIAL

AI Playground

Nine small machines to poke: fill-in-the-blank with BERT, the tokenizer's scissors, word-space arithmetic, nearest-play search, live judging of your own passages — with explanations — and a generator to compare.

Enter the playground →

The corpus

566single plays
190author entries
1496–1640first performances

Play texts are diplomatic transcriptions of the earliest editions from EEBO-TCP; every label comes from DEEP: Database of Early English Playbooks. Genre by the Annals of English Drama:

Comedy165
Tragedy118
History41
Masque40
Tragicomedy34
Civic Pageant18

About

HK

Heejin Kim

Associate Professor, Department of English Language and Literature, Kyungpook National University
Director, Digital Humanities Engineering Center

This site accompanies the workshop Training and Fine-Tuning Large Language Models for Early Modern Drama Research. Models run live on the research group's GPU server; every result on this site is computed for your input at the moment you ask.

Referencing this project

Please cite: Heejin Kim, Interpretable Deep Learning Stylometry of Early Modern English Drama, Kyungpook National University — https://stylometry.digihumeng.org. Code and data accompany a forthcoming article and will be archived openly (with a DOI) upon publication.