Instructions to use model-attribution-challenge/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use model-attribution-challenge/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="model-attribution-challenge/bert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("model-attribution-challenge/bert-base-cased") model = AutoModelForMaskedLM.from_pretrained("model-attribution-challenge/bert-base-cased") - Notebooks
- Google Colab
- Kaggle
Commit ·
d0d2939
1
Parent(s): 28e10c1
Adding `safetensors` variant of this model
Browse filesThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert
This new file is equivalent to `pytorch_model.bin` but safe in the sense that
no arbitrary code can be put into it.
These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.
If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions
Feel free to ignore this PR.
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1d8bdcee6021e2c25f0325e84889b61c2eb26b843eef5659c247af138d64f050
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size 435755784
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