Instructions to use l3cube-pune/odia-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/odia-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/odia-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/odia-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/odia-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5daeee9be0efd8ff0229485a533baf24bf26bee3154b2905bfd0eafa6e2d0f48
- Size of remote file:
- 951 MB
- SHA256:
- 323d6490936ad6ab71878b32516150f00ed0263a89eb79b21eeb201e1d05475b
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