Instructions to use l3cube-pune/hing-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hing-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hing-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hing-roberta") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hing-roberta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8d6965eb9a670e1729b5ae7631219be63b1aa7af88a71f11c79eb601885fe33
- Size of remote file:
- 1.11 GB
- SHA256:
- 766fccd55667fc375e4be070ca238361cfb5111bbfe1390ff316555964ae2cbc
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