Instructions to use pritamdeka/mDeBERTa-v3-base-Assamese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pritamdeka/mDeBERTa-v3-base-Assamese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pritamdeka/mDeBERTa-v3-base-Assamese")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pritamdeka/mDeBERTa-v3-base-Assamese") model = AutoModelForMaskedLM.from_pretrained("pritamdeka/mDeBERTa-v3-base-Assamese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aced8de4ef379ddb3230038bfa669caa759a7a6db978780157f5dc49f91cbbe1
- Size of remote file:
- 5.18 kB
- SHA256:
- 20b5fe4c806a04b3ecf26f46cd1fb2737a3b1b8b8ae04ffe12138aa779052c79
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