Instructions to use lucazed/LernnaviBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lucazed/LernnaviBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lucazed/LernnaviBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lucazed/LernnaviBERT") model = AutoModelForMaskedLM.from_pretrained("lucazed/LernnaviBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffe4716aeb081236f2d5395d09a25ed5e33c4343f41f84b5f9b78e9da5e24df5
- Size of remote file:
- 440 MB
- SHA256:
- e1a2719f00a8e4cc921d195f004e7073717197d4c2126417a6d3eadc0e47c16d
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