Instructions to use epfl-ml4ed/LernnaviBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epfl-ml4ed/LernnaviBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="epfl-ml4ed/LernnaviBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("epfl-ml4ed/LernnaviBERT") model = AutoModelForMaskedLM.from_pretrained("epfl-ml4ed/LernnaviBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edb3c73e3643c99ef6a3bf4c61700558b65a50e3f00de31e13f205e3441071ce
- Size of remote file:
- 4.73 kB
- SHA256:
- 85d83c671b2bbacbfe3db48bccb8124e9e13183786aff9f1a7868243d6d90e40
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