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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/finest-bert") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/finest-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b3d9fad20a285f738bedc02248ab9623eaa47011f1b668e33d5e9423674b7c0
- Size of remote file:
- 577 MB
- SHA256:
- ac954ec7dd9335e743b824734d51667eba5a8bf24424f14dd8c60342c0592be1
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