Instructions to use rahular/varta-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahular/varta-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rahular/varta-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rahular/varta-bert") model = AutoModelForMaskedLM.from_pretrained("rahular/varta-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rahular/varta-bert: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/rahular/varta-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rahular/varta-bert/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/rahular/varta-bert/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- 02b51fd0ae95dc760655eb035161ed4247711f70231e9a5355976ad23a7860ef
- Size of remote file:
- 738 MB
- SHA256:
- 7aab456499c2335062cef7402abf86c96a3f68489fdd62e8237f827d44893e4c
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