# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Livingwithmachines/bert_1760_1850")
model = AutoModelForMaskedLM.from_pretrained("Livingwithmachines/bert_1760_1850")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Neural Language Models for Nineteenth-Century English: bert_1760_1850
Introduction
BERT model trained on a large historical dataset of books in English, published between 1760-1850 and comprised of ~1.3 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-with-machines/histLM
License
The models are released under open license CC BY 4.0, available at https://creativecommons.org/licenses/by/4.0/legalcode.
Funding Statement
This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1).
Dataset creators
Kasra Hosseini, Kaspar Beelen and Mariona Coll Ardanuy (The Alan Turing Institute) preprocessed the text, created a database, trained and fine-tuned language models as described in the accompanying paper. Giovanni Colavizza (University of Amsterdam), David Beavan (The Alan Turing Institute) and James Hetherington (University College London) helped with planning, accessing the datasets and designing the experiments.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Livingwithmachines/bert_1760_1850")