Instructions to use Shushant/nepaliBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shushant/nepaliBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Shushant/nepaliBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Shushant/nepaliBERT") model = AutoModelForMaskedLM.from_pretrained("Shushant/nepaliBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 052b85b6219d650f086a0f4812f76d62478949c9bbd39c761777c3acb7ada83c
- Size of remote file:
- 2.8 kB
- SHA256:
- 608d06ba67ba6e1bb4ba0aba3e36b12b3f473d335dd1f8e5d8f5a98843fcbc27
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.