Instructions to use EMBEDDIA/finest-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBEDDIA/finest-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EMBEDDIA/finest-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/finest-bert") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/finest-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from EMBEDDIA/finest-bert: direct link, hf CLI and curl.
- Browser
- Download file 575 MB
-
https://huggingface.co/EMBEDDIA/finest-bert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://EMBEDDIA/finest-bert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/EMBEDDIA/finest-bert/resolve/main/flax_model.msgpack
575 MB
- Xet hash:
- c26d897e6bddcbd1520bfc68061dc50495a50591e2d62b06fcca4af406517f46
- Size of remote file:
- 575 MB
- SHA256:
- d6c0cb68f8481034db70684ec7130cd8b1034859d44acbb57e2b9ed7c37fbcf5
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