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:
- 0f8a2d6cbab00984ca68e497f07fe29d5a0d9bf89ac7a4b8bedbf86eb42ee1a4
- Size of remote file:
- 1.26 kB
- SHA256:
- 6f22ecfc72f7216b38fc49b8b1e7e7596473306fc34ae242981d936544ea51f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.