Instructions to use pritamdeka/muril-base-cased-assamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pritamdeka/muril-base-cased-assamese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pritamdeka/muril-base-cased-assamese")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pritamdeka/muril-base-cased-assamese") model = AutoModelForMaskedLM.from_pretrained("pritamdeka/muril-base-cased-assamese") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb25de21d9d94e0ff25068e19b17f9ac28971f29ce5a2a2dab3e367e1572d0d7
- Size of remote file:
- 5.18 kB
- SHA256:
- c7f14d1d4ab23358fb655d6b451b96dfc8de5e4a244f655a8173f25485d49272
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