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:
- cc5dd06915a4720ad5231e7b06841bd01adfb957a7632a42e6928c1bed0ab0b5
- Size of remote file:
- 1.11 GB
- SHA256:
- d7c9dd70471d8391cf510734cc1757fcca567972489ff5ef867a8eca33fde1df
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