Instructions to use mrm8488/codeBERTaJS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/codeBERTaJS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mrm8488/codeBERTaJS")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/codeBERTaJS") model = AutoModelForMaskedLM.from_pretrained("mrm8488/codeBERTaJS") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbddde93f5636fe1c21e293bee22ed3bef68e68c7e929f6a77b308da2e40f1b0
- Size of remote file:
- 336 MB
- SHA256:
- f6716a6ee3e7c29967071d32b9e209d73a8ec0fbc43892625caf2f4005d0324d
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