Instructions to use sbunlp/fabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbunlp/fabert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sbunlp/fabert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sbunlp/fabert") model = AutoModelForMaskedLM.from_pretrained("sbunlp/fabert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 460a52336866903ceeb1d5328abd7e64cc5bcccef243fa527c74832a145d2bc2
- Size of remote file:
- 498 MB
- SHA256:
- 6006d6b0a41d6ed43b40d7cf4dc9c842b9fd7c4bb74eada075b86c68b8387429
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