Instructions to use dbmdz/bert-base-historic-english-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-base-historic-english-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-base-historic-english-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-historic-english-cased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-base-historic-english-cased") - Notebooks
- Google Colab
- Kaggle
๐จ Notice: After re-checking this model again, it seems that the model is not working very well. E.g. MLM predictions are very likely to predict [UNK] token, which is
actually not good.
We will update this model soon. For now, please use the bigscience-historical-texts/bert-base-blbooks-cased instead, as it was pretrained on the same corpus.
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