Instructions to use l3cube-pune/gujarati-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/gujarati-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/gujarati-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/gujarati-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/gujarati-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9991923a1b113532a4839817e33bdb40ecc797cb16fc37b99b7dd49dbd3dbec1
- Size of remote file:
- 951 MB
- SHA256:
- 746e180442f35a8c83877cc6dfe0975705448d21f1c459f41cebd4856d3751ce
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