This generative text model was trained using Andrej Karpathy's code on texts of German Hexameter. Models of this type well represent individual style.
Model was trained with size 512 and 3 layers, dropout 0.5.
Usage
The procedure for installing the required software is described by Karpathy, torch is required, the code is written in lua. Be careful, versions of libraries written many years ago are used!
th sample.lua hexameter_lm_lstm_epoch80.00_1.3702.t7
Train data
Hexameter lines extracted from large collection of German verses running by Thomas Haider. Included in this repository as input.txt.
What for?
In an era of winning Transformers, ancient RNN models seem archaic. But I see that they still work better than modern architectures with such important categories from the humanities point of view as poetic style.
Dataset
There is a repository that publishes German poetic texts generated by RNN LSTM models (inclding this one) with different temperature.
Publication
There are some texts explaining the goal o these poetic experiments and their place in the history of human culture.
"Der digitale Superdichter. Vor 250 Jahren wurde Friedrich Hölderlin geboren. Heute kann Computertechnik neue Gedichte im Hölderlin-Sound generieren. Ein Werkstattbericht" Die Literarische Welt, 14 March 2020, p. 29. (included in this repository as pdf)
Orekhov, Boris, and Frank Fischer. "Neural reading: Insights from the analysis of poetry generated by artificial neural networks." Orbis Litterarum 75.5 (2020): 230-246. DOI: 10.1111/oli.12274
BibTeX entry and citation info
@article{orekhov2020neural,
title={Neural reading: Insights from the analysis of poetry generated by artificial neural networks},
author={Orekhov, Boris and Fischer, Frank},
journal={Orbis Litterarum},
volume={75},
number={5},
pages={230--246},
year={2020},
publisher={Wiley Online Library}
}