Instructions to use l3cube-pune/kannada-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/kannada-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/kannada-bert-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/kannada-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/kannada-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dae2308eaf4b8f1bd80787bc794d1ed4ec1195700d133095ca4d7356a2aa100b
- Size of remote file:
- 504 MB
- SHA256:
- ea1979300b1bbd2fcd3288b26931b29bc4facac952d9c70ce1b40e69a81d8e3d
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