Instructions to use aking11/hyebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aking11/hyebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aking11/hyebert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aking11/hyebert") model = AutoModelForMaskedLM.from_pretrained("aking11/hyebert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c53ab2eed341c6c53d1edb0aef59fd103d96a6fcc60fb10efbf90481d20aac1
- Size of remote file:
- 266 MB
- SHA256:
- 7813466c312b936ed599fa4ff9d6b2d8fccf34396255207afb0800d79b9c727d
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