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:
- f851bdcfd7e091f2beabaad140dbbb1539c824e5a4b3ea34c9bdda79cd131aa7
- Size of remote file:
- 334 MB
- SHA256:
- c383388b4b4c45eebbc265ad599cfa75f979a41a2d11509b9a50d0c5831a1564
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