Instructions to use Rajan/NepaliBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rajan/NepaliBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rajan/NepaliBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Rajan/NepaliBERT") model = AutoModelForMaskedLM.from_pretrained("Rajan/NepaliBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Rajan/NepaliBERT: direct link, hf CLI and curl.
- Browser
- Download file 328 MB
-
https://huggingface.co/Rajan/NepaliBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Rajan/NepaliBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Rajan/NepaliBERT/resolve/main/pytorch_model.bin
328 MB
- Xet hash:
- f8b7c51e082a1cab3e3122b27f57859b6df0e86b1c8e7dbc3c433ab6b98d7913
- Size of remote file:
- 328 MB
- SHA256:
- 2d6afbf12c5b29586906e14fd5821a82ab15c5a7924d113d74224a394d20301f
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