Instructions to use emanjavacas/MacBERTh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emanjavacas/MacBERTh with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emanjavacas/MacBERTh", dtype="auto") - Notebooks
- Google Colab
- Kaggle
MacBERTh
This model is a Historical Language Model for English coming from the MacBERTh project.
The architecture is based on BERT base uncased from the original BERT pre-training codebase. The training material comes from different sources including:
- EEBO
- ECCO
- COHA
- CLMET3.1
- EVANS
- Hansard Corpus
with a total word count of approximately 3.9B tokens.
Details and evaluation can be found in the accompanying publications:
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