
This model has been trained as part of the ongoing edition project Burchards Dekret Digital (www.burchards-dekret-digital.de), funded by the Academy of Sciences and Literature Mainz. It is the project's first high-quality model specifically designed to produce a graphematic transcription based on a predefined set of special characters (https://github.com/michaelscho/transpy?tab=readme-ov-file#special-characters) in accordance with the MUFI standard. The model was trained on five 11th-century manuscripts that can be traced to the episcopal scriptorium in Worms: Bamberg, SB, Msc.Can.6 (https://mdz-nbn-resolving.de/urn:nbn:de:bvb:12-bsb00140701-0), Frankfurt, UB, Ms. Barth. 50 (https://sammlungen.ub.uni-frankfurt.de/msma/urn/urn:nbn:de:hebis:30:2-12488), Köln, EDD, Cod. 119 (https://digital.dombibliothek-koeln.de/urn/urn:nbn:de:hbz:kn28-3-3241), Vatican, BAV, Pal.lat.585 (https://digi.vatlib.it/mss/detail/Pal.lat.585), and Vatican, BAV, Pal.lat.586 (https://digi.vatlib.it/mss/detail/Pal.lat.586). However, it also works well as a base model for later medieval scripts. The model was trained by Dr. Michael Schonhardt (Universität Kassel, https://orcid.org/0000-0002-2750-1900). Transcriptions were provided and proofread by Helena Geitz, Daniel Gneckow, Dr. Andreas Grote, Prof. Dr. Lotte Kéry, Dr. Birgit Kynast, Dr. Hans-Christian Lehner, Dr. Melanie Panse-Buchwalter, Michaela Parma, Dr. Cornelia Scherer, Dr. Michael Schonhardt and Dr. des. Elena Vanelli. The project is led by Prof. Dr. Ingrid Baumgärtner, Prof. Dr. Klaus Herbers and Prof. Dr. Ludger Körntgen. The model was trained in 54 epochs using a learning rate of 0.0008 and a batch size of 64.
kraken_pytorch
kraken_pytorch
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
