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:
- 1150e3bc7eff5d06fc934ccc5bdc6d251d41ba2fc58c701e2e29c8e580867601
- Size of remote file:
- 951 MB
- SHA256:
- 7e2979552180f7afb59dd201edcea288d5c0eff2e4c9c9b8e65fb3c5eb253c85
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