Instructions to use UBC-NLP/serengeti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/serengeti with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UBC-NLP/serengeti")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/serengeti") model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/serengeti", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2fded2dc2e1697368e91acff13c183457eafac4b5e3e2f40e084790ee79c78ac
- Size of remote file:
- 18 MB
- SHA256:
- 0abe075e3519bf5297d2d409e124077afaa29aa2ae081fbbea37418b2dcb8137
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