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")# 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 tokenizer.json from rahular/varta-bert: direct link, hf CLI and curl.
- Browser
- Download file 3.97 MB
-
https://huggingface.co/rahular/varta-bert/resolve/0642e23de6423489ae64893c5a78121a6efeb5b8/tokenizer.json
- Command line
-
hf download hf://rahular/varta-bert@0642e23de6423489ae64893c5a78121a6efeb5b8/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/rahular/varta-bert/resolve/0642e23de6423489ae64893c5a78121a6efeb5b8/tokenizer.json
3.97 MB
- Xet hash:
- 1fbac03e49e1c690aa12b78e32a546c16dc6b3253dde4082a5d864878a883048
- Size of remote file:
- 3.97 MB
- SHA256:
- d4645b564d21ce48d786040c6453555e3bee8960865a91e4b36d3dbfddbcafd2
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