Instructions to use l3cube-pune/assamese-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/assamese-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/assamese-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/assamese-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/assamese-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69a88460fcb2a4d27a570cbafad8d0877ce808fc60d1fafcbebf6e5dbb611001
- Size of remote file:
- 951 MB
- SHA256:
- 942883c739351ebbc1881e894e45c44e500ada949564aac1b60ded1858687911
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