Instructions to use model-attribution-challenge/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use model-attribution-challenge/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="model-attribution-challenge/bert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("model-attribution-challenge/bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("model-attribution-challenge/bert-base-uncased") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:68d45e234eb4a928074dfd868cead0219ab85354cc53d20e772753c6bb9169d3
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size 440449768
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