This dataset contains four types of neural language models trained on a large historical dataset of books in English, published between 1760-1900 and comprised of ~5.1 billion tokens. The language model architectures include static (word2vec and fastText) and contextualized models (BERT and Flair). For each architecture, we trained a model instance using the whole dataset. Additionally, we trained separate instances on text published before 1850 for the two static models, and four instances considering different time slices for BERT. Github repository: https://github.com/Living-with-machines/histLM.
Contributor (organisation)
Living with Machines
Project(s)
Living with Machines
Funder
Funder name
Awards
Arts and Humanities Research Council (AHRC)
AH/S01179X/1
Engineering and Physical Sciences Research Council (EPSRC)
