Instructions to use nbroad/muril-bigbird-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/muril-bigbird-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nbroad/muril-bigbird-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nbroad/muril-bigbird-base") model = AutoModelForMaskedLM.from_pretrained("nbroad/muril-bigbird-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 751aff134b2305b4cd87399815f746964002f46c08464e9a5ea7fc5ed07a47cc
- Size of remote file:
- 962 MB
- SHA256:
- 8508f5197970464547fe4f07fd8593cb903eaadd59adee6038e8a20fa5c122ee
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