Instructions to use vasudevgupta/amazon-ml-hack-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasudevgupta/amazon-ml-hack-bert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, sifier tokenizer = AutoTokenizer.from_pretrained("vasudevgupta/amazon-ml-hack-bert-base") model = sifier.from_pretrained("vasudevgupta/amazon-ml-hack-bert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from vasudevgupta/amazon-ml-hack-bert-base: direct link, hf CLI and curl.
- Browser
- Download file 468 MB
-
https://huggingface.co/vasudevgupta/amazon-ml-hack-bert-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://vasudevgupta/amazon-ml-hack-bert-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/vasudevgupta/amazon-ml-hack-bert-base/resolve/main/flax_model.msgpack
468 MB
- Xet hash:
- 0a535048d90d1d1831a506fbc2e6fdfac3cd061d88fd81efff0d70becd68d209
- Size of remote file:
- 468 MB
- SHA256:
- 9d8a9f16001d775f53ee1f152aaa016cbecad47c7dbab35bc52b3ecd15da5d42
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