Instructions to use lucazed/lernnavi_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lucazed/lernnavi_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lucazed/lernnavi_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lucazed/lernnavi_bert") model = AutoModelForMaskedLM.from_pretrained("lucazed/lernnavi_bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d0f67be483cde6215160994d67fb0c5d5719a0a569a77677c4a206fb1740f13
- Size of remote file:
- 440 MB
- SHA256:
- 86588c56ab84926ba457a810e3fb9a80dc8b9943de97d0f77574a0e0df31e766
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