Fill-Mask
Transformers
Safetensors
bertbm
modernbert
masked-lm
neural-operator
fourier-neural-operator
graph-neural-network
geometric-deep-learning
riemannian
event-driven
custom_code
Instructions to use scairesearch/bertbm-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scairesearch/bertbm-100m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="scairesearch/bertbm-100m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("scairesearch/bertbm-100m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __pycache__/modeling_bertbm.cpython-310.pyc from scairesearch/bertbm-100m: direct link, hf CLI and curl.
- Browser
- Download file 8.85 kB
-
https://huggingface.co/scairesearch/bertbm-100m/resolve/main/__pycache__/modeling_bertbm.cpython-310.pyc
- Command line
-
hf download hf://scairesearch/bertbm-100m/__pycache__/modeling_bertbm.cpython-310.pyc
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curl -L -o modeling_bertbm.cpython-310.pyc https://huggingface.co/scairesearch/bertbm-100m/resolve/main/__pycache__/modeling_bertbm.cpython-310.pyc
8.85 kB
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