Instructions to use miladfa7/sanay-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miladfa7/sanay-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="miladfa7/sanay-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("miladfa7/sanay-bert") model = AutoModelForMaskedLM.from_pretrained("miladfa7/sanay-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from miladfa7/sanay-bert: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/miladfa7/sanay-bert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://miladfa7/sanay-bert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/miladfa7/sanay-bert/resolve/main/flax_model.msgpack
652 MB
- Xet hash:
- 62a49e7be7f322952fc447da3ea983d498aada63c7706107a72e23d50ead2edd
- Size of remote file:
- 652 MB
- SHA256:
- a8534236ebe80c1cfb9c1083a2d24b590292e26f6cef3dad8bbc9a9033bb7e29
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